A cost-based optimization of a fiberboard pressing plant ...eprints.qut.edu.au/30417/1/Monardo_Giani_Thesis.pdf · A cost-based optimization of a fiberboard pressing plant using Monte-Carlo

By

MONARDO GIANI

Under the Supervision of

Professor Lin Ma and

Professor Prasad Yarlagadda

A Thesis Submitted for the degree of Master of Engineering

April 2009

A cost-based optimization of a fiberboard pressing plant using Monte-Carlo simulation (A reliability program)

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A cost-based optimization of a fibreboard pressing plant using Monte-Carlo simulation (A reliability program)

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I hereby declare that this submission is my own work and to the best of my

knowledge it contains no materials previously published or written by another person,

or substantial proportions of material which have been accepted for the award of any

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acknowledgement is made in the thesis. Any contribution made to the research by

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the thesis.

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Abstract

In this research the reliability and availability of fiberboard pressing plant is

assessed and a cost-based optimization of the system using the Monte- Carlo

simulation method is performed.

The woodchip and pulp or engineered wood industry in Australia and around

the world is a lucrative industry. One such industry is hardboard. The pressing

system is the main system, as it converts the wet pulp to fiberboard. The

assessment identified the pressing system has the highest downtime throughout the

plant plus it represents the bottleneck in the process.

A survey in the late nineties revealed there are over one thousand plants

around the world, with the pressing system being a common system among these

plants. No work has been done to assess or estimate the reliability of such a

pressing system; therefore this assessment can be used for assessing any plant of

this type.

Keywords: Hardboard, Fiberboard, Pressing system, Reliability, Availability,

Monte-Carlo simulation.

Acknowledgements I would like to acknowledge the following key persons for their contribution in

completing this research: Professor Lin Ma, my principle supervisor for her much appreciated constant

guidance and assistance throughout the lengthy research period. Professor Dr Professor Prasad Yarlagadda my associate supervisor for his

guidance and assistance. Mr. Mick Drew, Director of ARMS reliability engineers for providing the simulation

software. Mr. Wayne Chilton, Maintenance Manager from Australian Hardboards Limited.

Wayne provided the missing and very much needed information about the hardboard manufacturing equipment performance history.

Mrs. Julie McGillivray, work colleague from Australian Hardboards Limited, for

her extraordinary effort and time in completing this research. To my beloved wife, Abeer for her patience with me all the time taken to

complete this work.

A cost-based optimization of a fiberboard pressing plant using Monte-Carlo simulation (A reliability program) - Monardo Giani

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TABLE OF CONTENTS

CHAPTER ONE – INTRODUCTION………………………………………………..11 1.1 Overview of the woodchip and pulp industry……………………….11

1.2 Products produced at the plant………………………………………12

1.3 Definition of the research problem and its importance...................14

1.4 Research objectives…………………………………………………..16

1.5 Expected outcome of the research………………………………….17

CHAPTER TWO - LITERATURE REVIEW………………………………………...18 2.1 Introduction ................................................................................... 18

2.2 Maintenance management ............................................................ 20

2.3 The scope of maintenance ............................................................ 24

2.3.1 Frameworks .................................................................................. 26

2.3.1.1 Reliability-Centered Maintenance (RCM) ...................................... 27

2.3.1.2 Total Productive Maintenance ....................................................... 28

2.3.1.3 Business-Centered Maintenance (BCM)…………………………….29

2.3.1.4 Maintenance Excellence ............................................................... 30

2.3.1.5 Other frameworks .......................................................................... 32

2.3.2 Reliability Assessment and analysis ............................................. 33

2.3.2.1 Analytical models .......................................................................... 34

2.3.2.1.1 Basic principles of probability based ............................................. 35

2.3.2.1.2 Markovian theory ........................................................................... 37

2.3.2.1.3 Bayesian theory ............................................................................ 38

2.3.2.1.4 Poisson process ............................................................................ 41

2.3.2.1.5 Models based on the Condition monitoring data ........................... 42

2.3.2.2 Other techniques ........................................................................... 43

2.3.2.2.1 Condition monitoring and fault diagnosis (CMFD) ......................... 43

2.3.2.2.2 Fault tree and root cause analysis ................................................ 44

2.3.2.2.3 Reliability block diagram (RBD) ..................................................... 45


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2.3.2.2.4 Failure mode, effects and criticality analysis (FMECA) ................. 46

2.3.2.2.5 Monte Carlo simulation .................................................................. 47

2.3.3 Maintenance optimization .............................................................. 48

2.3.3.1 Cost based optimization policies ................................................... 48

2.3.3.2 Risk based optimization policies ................................................... 49

2.3.3.3 Other Maintenance strategies ....................................................... 50

2.3.3.4 Discussion ..................................................................................... 51

CHAPTER THREE - FIBREBOARD PRODUCTION PROCESS ....................... 53 3.1 Introduction ................................................................................... 53

3.2 The Hardboard production process ............................................... 53

3.2.1 Wood chipping and mixing ............................................................ 54

3.2.2 Pulp preparation and storage stage .............................................. 54

3.2.3 Pulp forming stage ........................................................................ 55

3.2.4 Sizing stage ................................................................................... 56

3.2.5 Wet mat transfer stage (plate circuit) ............................................ 57

3.2.6 The pressing stage ........................................................................ 57

3.2.7 Tempering stage ........................................................................... 58

3.2.8 Humidification stage ...................................................................... 59

3.2.9 Grading and secondary process ................................................... 59

3.3 System selection ........................................................................... 60

3.4 An overview of the selected system (Pressing plant) .................... 62

3.4.1 Press Technical Data: ................................................................... 62

3.4.2 Press technical description ............................................................ 62

3.4.3 System functionality ...................................................................... 64

3.5 System boundaries definition ........................................................ 67

CHAPTER FOUR - SYSTEM RELIABILITY ASSESSMENT…………………….70

4.1 Introduction ................................................................................... 70

4.1.1 Functional Failure Mode, Effect and Criticality Analysis (FMECA) 70

4.1.2 Modeling the failure data ............................................................... 71

4.1.2.1 Press Rams ................................................................................... 73


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4.1.2.1.1 Estimating the MTBF ..................................................................... 73

4.1.2.1.2 Estimating the MTTR..................................................................... 75

4.1.2.2 Solenoid valves S1, S2, S3, S4 and S10 ...................................... 76

4.1.2.2.1 Estimating the MTTF ..................................................................... 76

4.1.2.3 Low-pressure pumps ..................................................................... 76



4.1.2.4 High-pressure pumps 1-4 .............................................................. 80

4.1.2.4.1 Estimating the failure model .......................................................... 80


4.1.2.5 Boiler set ....................................................................................... 84



4.1.2.6 Pre-fill and exhaust valve no.6………………………………………..89

4.1.2.6.1 Estimating failure model MTBF……………………………………….89

4.1.2.6.2 Estimating repair model MTTR ...................................................... 90

4.1.2.7 Blocking magnet ............................................................................ 91

4.1.2.7.1 Estimating failure model parameters ............................................. 91

4.1.2.8 High-pressure pump no. 5 ............................................................. 93

4.1.2.9 Non-return valves .......................................................................... 94

4.1.2.10 Thruster valves 1- 4 ...................................................................... 94



4.1.2.11 Tubular guides .............................................................................. 99

4.1.2.11.1 Estimating MTBF ........................................................................... 99

4.1.2.11.2 Estimating MTTR ........................................................................ 102

4.1.2.12 Heating platens leaks .................................................................. 104

4.1.2.12.1 Estimating the MTBF ................................................................... 104

4.1.2.12.2 Estimating the MTTR................................................................... 105

4.1.2.13 Link pipes .................................................................................... 106



4.1.2.14 Bleeding valve no.7 ..................................................................... 108


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4.1.2.14.1 Estimating the MTTF ................................................................... 108

4.1.2.15 Temperature sensor .................................................................... 109

4.1.2.16 Exhaust fan bearings .................................................................. 109

4.1.2.17 Pressure sensor .......................................................................... 110

4.1.2.18 Timer ........................................................................................... 111

4.1.2.19 Hydrauphore tank ........................................................................ 111



4.1.2.20 Valve no.11 ................................................................................. 115



4.1.2.21 Air compressor ............................................................................ 116



4.1.2.22 Control valves no.8 & 9 ............................................................... 118

4.1.2.23 Change over valve no.5 .............................................................. 121

4.1.2.24 PLC ............................................................................................. 121

4.1.2.25 Piping system .............................................................................. 122

4.1.2.26 Platens off position ...................................................................... 122



4.1.2.27 Hydrauphore relief valve ............................................................. 124

4.1.2.28 Hydraulic storage tank parameters ............................................. 125

4.1.3 Fault Tree Analysis (FTA) ........................................................... 125

4.1.3.1 Constructing the fault tree model ................................................ 127

4.1.3.2 Estimating the system probability of failure from FTA ................. 131

4.1.4 Reliability Block Diagram Analysis .............................................. 135

4.1.4.1 Estimating the system reliability .................................................. 136

4.1.5 Estimating the system Availability ............................................... 140

4.2 Monte Carlo Simulation ............................................................... 143

4.3 Evaluating the system performance ............................................ 146

4.4 Optimizing the system ................................................................. 154

4.4.1 Mathematical models and equations ........................................... 155


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4.4.1.1 Modeling the optimal replacement times for equipment which

it’s operating cost increases with usage ...................................... 155

4.4.1.2 Modeling optimal preventive replacement interval of an item

subject to breakdown .................................................................. 157

4.4.1.3 Modeling of the optimal spare parts preventive replacement

age and constant failure interval ................................................. 159

4.4.1.4 Modeling the optimal inspection frequency ................................. 160

4.4.1.5 Availability ................................................................................... 162

4.5 Obtaining the optimized system results ....................................... 162

CHAPTER FIVE - SENSITIVITY ANALYSIS .................................................... 167

5.1 Introduction ................................................................................. 167

5.2 Comparing system data with and without dependencies before

optimization ................................................................................. 167

5.2.1 Estimating the simulated system reliability

5.2.2 Estimating the simulated system Availability.................................170

5.3 Comparing the number of failures and downtime..........................174

5.4 Criticality values estimation...........................................................175

5.5 Comparing the different cost associated with the system before

and after optimization....................................................................176

5.6 Conclusions...................................................................................177

CHAPTER SIX – CONCLUSIONS.....................................................................178

6.1 Introduction....................................................................................178

6.2 Major findings................................................................................179

6.3 Limitations and Uncertainties.........................................................182

6.4 Future studies................................................................................184


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REFERENCES .................................................................................................. 186 APPENDIX 1 ..................................................................................................... 198 APPENDIX 2 ..................................................................................................... 199 APPENDIX 3 ..................................................................................................... 200 APPENDIX 4 ..................................................................................................... 201 APPENDIX 5 ..................................................................................................... 203 APPENDIX 6 ..................................................................................................... 211 APPENDIX 7 ..................................................................................................... 212 APPENDIX 8 ..................................................................................................... 214 APPENDIX 9 ..................................................................................................... 222 APPENDIX 10 ................................................................................................... 225 APPENDIX 11………………………………………………………………………...227 APPENDIX 12…………………………………………………………………….…..229


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INDEX OF FIGURES

Figure 1: Changing expectations of maintenance (Moubray 1997) .......... 18

Figure 2: Asset management versus maintenance management (van

Voorthuysen 2005) ..................................................................... 19

Figure 3: Growing expectations of maintenance (Moubray 1997) ............ 22

Figure 4: Routine and non routine maintenance activities (Hasting 2001) 25

Figure 5: Maintenance science classification (Sun 2006) ......................... 26

Figure 6: The RCM process (Jardine 2006) ............................................. 28

Figure 7: Modified structural approach to achieving maintenance

excellence by Campbell 1995. .................................................. 30

Figure 8: A schematic diagram of the production flow process ................ 59

Figure 9: The research process flow chart ............................................... 60

Figure 10: breakdown of the plant areas downtime .................................... 61

Figure 11: Schematic drawing of the press ................................................ 64

Figure 12: Block diagram of the pressing process ...................................... 65

Figure 13: illustrates the press cycle working pressures ............................ 67

Figure 14: illustrates the pressing plant and its components

(Sunds defibrators-Sweden) ....................................................... 68

Figure 15: A schematic diagram of the system boundaries ........................ 68

Figure 16: The press rams weibull plot ....................................................... 74

Figure 17: Testing the LPPs data against the HPP .................................... 77

Figure 18: Low pressure pumps log TTF-log N(T) ...................................... 78

Figure 19: Low pressure pumps repair model graph .................................. 80

Figure 20: Testing the HPPs 1-4 data against the HPP ............................. 81

Figure 21: HPPs 1-4 log failure data .......................................................... 81

Figure 22: HPPs 1-4 repair model .............................................................. 84

Figure 23: Boiler TBF/N(T) graph ............................................................... 85

Figure 24: Boiler log t-log N(T) ................................................................... 86

Figure 25: Boiler repair model graph .......................................................... 89

Figure 26: Valve no.6 Weibull distribution graph ........................................ 90

Figure 27: V/v no.6 exponential distribution repair graph ........................... 91

Figure 28: Blocking magnet failure model graph ........................................ 92


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Figure 29: Thruster valves TBF against N(T) ............................................. 95

Figure 30: Thruster valves log TBF-log N(T) .............................................. 96

Figure 31: Thruster valves log-normal repair model plot ............................ 98

Figure 32: Tubular guides TBF-N(T) ........................................................ 100

Figure 33: Tubular guides log TBF-log N(T) ............................................. 101

Figure 34: Log-normal plot of tubular guides repair data .......................... 103

Figure 35: Heating platen exponential repair model ................................. 105

Figure 36: Link pipes repair model ........................................................... 108

Figure 37: Log-normal distribution of centrefugal pump and circuit breaker

failure rates (independent samples)(T.R.Moss) ....................... 108

Figure 38: Pressing system RBD ............................................................. 135

Figure 39: High pressure pumps 1-4 assembly ........................................ 136

Figure 40: Low pressure pumps ............................................................... 140

Figure 41: Low pressure assembly ........................................................... 140

Figure 42: Hydrauphore sub assembly ..................................................... 140

Figure 43: Heating system ....................................................................... 140

Figure 44: Remaining sub assembly ........................................................ 140

Figure 45: Press assembly ....................................................................... 140

Figure 46: Pressing plant simplified .......................................................... 140

Figure 47: Illustrates the used simulation sequence ................................. 145

Figure 48: Sub-systems unavailability profiles before optimization .......... 153

Figure 49: System cost profile before optimization ................................... 154

Figure 50: Short term deterministic optimization ...................................... 156

Figure 51: Optimal preventive replacement .............................................. 158

Figure 52: Optimal inspection frequency to maximize profit ..................... 161

Figure 53: Optimal inspection frequency to minimize downtime ............... 162

Figure 54: Unavailability profile of plant sub-system after optimization .... 165

Figure 55: System cost profile after optimization ...................................... 166


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INDEX OF TABLES

Table 1: Press rams failure data ................................................................ 73

Table 2: The press rams TTF .................................................................... 74

Table 3: Press rams repair times ............................................................... 75

Table 4: Low pressure pumps TTF ............................................................ 77

Table 5: Ranked ttf,N(T),log cumulative and log N(T) ............................... 77

Table 6: Low pressure pumps repair data ................................................. 79

Table 7: HPPs failure data re-arranged ..................................................... 81

Table 8: HPPs1-4 times to repaire ............................................................ 83

Table 9: Boiler TBF/N(T) ........................................................................... 85

Table 10: Boiler log N(T), log TBF ............................................................... 86

Table 11: Boiler repair data ......................................................................... 88

Table 12: TTF and TTR for valve no.6 ........................................................ 89

Table 13: Blocking magnet TTF .................................................................. 91

Table 14: Thruster valves TBFs .................................................................. 94

Table 15: Thruster valves TBFs against N(T) .............................................. 95

Table 16: Thruster valves log TBF against log N(T) .................................... 96

Table 17: Thruster valves repair data .......................................................... 98

Table 18: Tubular guides’ failure data ......................................................... 99

Table 19: Tubular guides TBF-N(T) ............................................................. 99

Table 20: Tubular guides Log TBF-Log N(T) ............................................. 100

Table 21: Tubular guides repair data ......................................................... 103

Table 22: Heating platens failure data ....................................................... 104

Table 23: Heating platens repair data ....................................................... 105

Table 24: Link pipes failure data ................................................................ 106

Table 25: Link pipes ranked repair times ................................................... 107

Table 26: Generic data from different sources .......................................... 113

Table 27: Ordered failure rates data estimates ......................................... 113

Table 28: Hydrophore tank design attributes ............................................. 114

Table 29: Operating attributes ................................................................... 114

Table 30: Environment attributes ............................................................... 114

Table 31: Design attributes ....................................................................... 117


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Table 32: Generic data from different sources .......................................... 119

Table 33: Ordered failure rates estimates ................................................. 119

Table 34: Design attributes ....................................................................... 120

Table 35: Operating attributes ................................................................... 120

Table 36: Environment attributes ............................................................... 120

Table 37: Platens’ failure data ................................................................... 123

Table 38: Platens’ off-position repair data ................................................. 124

Table 39: Illustrates obtaining TTF values by simulation ........................... 147

Table 40: System profile before optimizing ............................................... 152

Table 41: System effects data before optimization .................................... 153

Table 42: C(tr) values for different values of tr .......................................... 163

Table 43: C(tp) values for different values of tp ......................................... 164

Table 44: Press system effects data after optimization…………………...136

Table 45: System profile after optimization……………….………………...164

Table 46: Comparison of system reliability & availability with and without

dependencies before and after optimization………………………173

Table 47: The number of expected failures and downtime of the system

Before and after optimization……………………………………….175


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CHAPTER ONE - INTRODUCTION

1.1 Overview of the woodchip and pulp industry The woodchip and pulp or engineered wood industry in Australia

and around the world is a lucrative industry; the industry's turnover in

Australia was $9.91 billion, or around one per cent of GDP in 1992-93

(latest available data). The industry employs approximately 82,500

people, according to the latest labor force estimates from the Australian

Bureau of Statistics. It’s a mature industry with a strong market. Here in

Australia, the Australian timber industry is going through unprecedented

change. There are significant opportunities for growth in the production

and sales of high value timber products in all Australian species groups

[1].

One such product is hardboard, which is made of hardwood, i.e.

Fiberboard with a density exceeding 0.80g/cm3.

Hardboard is a high density 100% all-natural fiber board, also

known as Masonite™. Made from superior fibers, Masonite™ is

capable of bringing enhanced stability, water resistance and durability

into the designed systems of the modern building, joinery, furniture

markets. Masonite™ was developed in Laurel, Mississippi in 1920 by

William H Mason, an expert on wood derivatives and an associate of

Thomas Edison. Masonite™ has a high strength to thickness ratio,

producing a lightweight, versatile, multi-purpose building, fabrication

and packaging material. Hardboard production uses only naturally

occurring wood glues (lignin) to bond fibers, eliminating the need for

synthetic glues and resins. Masonite™ panels are dimensionally stable

in service and have higher moisture resistance properties than many

other wood based panel products [2].


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1.2 Products produced at the plant The plant produces different types of products. Listed below are

some of these products.

• Masonite™ Pegboard Masonite™ Pegboard has unlimited applications. It

is an ideal storage solution used Australia-wide from the

garage to the showroom.

• Masonite™ Readifix Not only is Readifix pre-primed, it's preconditioned

and has beveled edges so all that is needed is to butt

panels together and apply the top coat.

• Masonite™ Underlay Masonite™ Underlay is used to cover timber,

particleboard or concrete flooring. Masonite™ Underlay

is the only product that complies to Australian Standards

and is recommended by leading vinyl manufacturers.

• Masonite™ Standard Masonite™ Standard will withstand intermittent

wetting without loss of strength.


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• Masonite™ Tempered Masonite™ Tempered is tough, strong, indent and

moisture resistant.

• Masonite™ White-Cote Masonite™ White-Cote is a pre-finished moisture-

resistant board ideal for cabinetry, furniture backs and

bottoms.

• Masonite™ Chalkboard Masonite™ Chalkboard is used from commercial

signage to playroom blackboards.

• M4 & M6 Braceboard M4 Braceboard provides structural strength with

easy to follow nail markings and instructions on each

sheet and is competitively priced. Braceboard is used to

brace timber frames in brick veneer construction. M6 is

ideal for narrow paneling requirements i.e. next to

windows.


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• Deco Hardboard Specially designed panels which offer "design

flexibility" by blending popular holing configurations with

four major finish categories including timber veneer, vinyl,

laminates and Color-Cote (painted).

1.3 Definition of the research problem and its importance:

The predicted volume of hardwood pulpwood produced in

Australian plantations will increase from around 0.7 million cubic

meters per annum in the 1995-99 period to over 10 million cubic

meters per annum in the period 2035-39 [3]. The increase in the

population, the rising demand for the wood products to be used in the

housing industry [4], and since hardboard has established itself as a

reliable product for use in the dwelling construction, furniture and

cabinetry industry for its unique characteristics, it is forecast that it still

can retain its niche market if it can introduce new technologies and

reduce its maintenance cost.

While most wood products enjoy an increasing market, the

hardboard market is foreseen as losing ground to other products, the

reasons for this as cited by Gunnersen [5] are:

Competition from similar products.

The world wide situation of over-supply of medium density

fiberboard is maintaining downward pressure on prices in

those markets which both MDF and hardboard supply.

Selling prices not increasing, with costs rising by 4% p.a.

There are constraints with existing plant and infrastructure

on cost reductions in the manufacture of hardboard which


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put a floor under this industry's capability to compete on

price against products employing newer technology,

especially those using the dry process.

High levels of maintenance cost due to the age of the plants

still operating in Australia and the fact that production

involves a wet process.

For the above reasons, and the importance of this industry, this

research was undertaken. Searching the literature yielded no results of

any study has been done to improve the manufacturing process for

this type of industry.

A survey of the business in concern revealed that although the

organization is still capturing a good share of the market, but it’s

loosing this share in alarming levels due to the increasing unavailability

of the machines in its plant, and the increasing of its maintenance cost

to about 46% of its operating budget.

Fig 10 in section 3 illustrates the downtime of the different

process areas, revealed by the plant survey, with one major area, the

pressing system which is the highest contributor to the plant downtime.

The actual numbers were removed due to its sensitivity.

The press plant is used for transforming the wet pulp into dry

board, by draining the moisture out of the pulp by means of

compression and heat. The production of wood fiber by mixing wood

chips with water is known as the wet method.

The pressing plant has the highest downtime; therefore this area

will be targeted in this research. This is in line with the fact that the


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pressing plant is the most important part of the process, as in this part

the transformation of the pulp into board occurs, plus it represents the

bottleneck of the whole process, which led the company to increase

the capacity of the press to thirty openings. The new design has

increased the capacity, but introduced additional problems to the

system

1.4 Research objectives The objectives of the thesis are as follow:

1. Learn the principles of Reliability Centre Maintenance (RCM),

as part of asset management and apply it to real life project.

2. Optimize the performance of a complex repairable system

under imperfect repairs by reducing the cost of the associated

maintenance activities, and increasing the availability of the

system.

3. From experience as being Maintenance personnel on the shop

floor, people involved in the maintenance part of the business

are hands on. i.e. they are the ones who implements the

maintenance strategies, and with the continuing pressure to

have the equipment performing satisfactory, they do not have

the time to carry out detailed and thorough research to come up

with new models and methodologies to implement in order to

improve their systems, plus they need an easy to understand

applications in order to make the job less complicated, plus to

convince the tradesmen of the viability of implementing such

programs and how it’s going to reflects positively on the way

that they do the task.

4. To lay the foundation for further studies in this field for this type

of industry.


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1.5 Expected outcome of research

The expected outcome hoped to be achieved through this

research is:

• Optimizing the performance of the highest downtime section of a

hardboard plant by reducing the cost of its maintenance through

a reliability centered maintenance program by using Monte Carlo

method based simulation. Once this is achieved, it can be carried

out in the same method onto the other areas of the plant.

• An applicable reliability program that can be used by the

maintenance personnel in the engineered wood industry, able to

reduce the rising maintenance cost of the plant and bring it in-line

with the organization’s objectives to stay in business.

This program can also be applied to similar industry plants

around the world, as they all share the same manufacturing

concept.


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CHAPTER TWO - LITERATURE REVIEW 2. 2.1 Introduction

Asset management is a broad concept that is aimed at managing

return on investment [6].

Companies and organizations invest substantial amounts on

obtaining assets, and also on maintaining them, therefore they need

an effective maintenance plans to maintain these assets, prolong their

reliable life cycle as much as possible to achieve the intended

business objectives, such as provide safe working environment, better

quality product or service, have the maximum return on their

investment. And so on.

Tsang and Jardine [7]. Stated that the performance demand of

physical asset management has become more challenging as a result

of three developments:

Emerging trends of operation strategies;

Toughening societal expectations; and

Technological changes.

Figure 1: Changing expectations of maintenance (Moubray 1997)


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Figure 1 illustrates the changes expectations of maintenance. In

order to meet these demands, organizations need to focus on

improving the performance of their physical assets [8], maintenance

needed to respond to these demands and expectations changes, such

as the growing awareness of the extent to which equipment failure

effects safety and the environment, a growing awareness of the

connection between maintenance and product quality, and increasing

pressure to achieve high plant availability and to contain costs [9]. This

is achievable through processes re-engineering, optimizing

maintenance strategies, having the right human resources and

systems in place and proper planning and scheduling.

The objectives of asset management according to Van

Voorthuysen [10] are:

Minimize investment;

Minimize ownership costs;

Maximize commercial returns; and

Manage risks.

Figure 2: Asset management versus maintenance management (van

Voorthuysen 2005)


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2.2 Maintenance management Maintenance management is a sub section of the asset

management big picture, and in this thesis we will be concentrating on

this section of the asset management.

Maintenance is defined in the American Federal Standard 1037C

and from Military Standard 188 as: The care and servicing by

personnel for the purpose of maintaining equipment and facilities in

satisfactory operating condition by providing for systematic inspection,

detection, and correction of incipient failures either before they occur

or before they develop into major defects. Including tests,

measurements, adjustments, and parts replacement, performed

specifically to prevent faults from occurring [11], and Geraerd [12],

defined it as “Maintenance is all activities aimed at keeping an item in,

or restoring it to, the physical state considered necessary for the

fulfillment of its production function”, while the British Standards

Institution defines maintenance as “the combination of all technical and

administrative action intended to retain an item in, or restore it to, a

state in which it can perform its required function” [13].

Maintenance management covers every stage of the life cycle of

systems that include plant, machinery, equipment and the facilities that

hosts them, the Specification, acquisition, planning, operation,

performance evaluation, improvement, and disposal. [14]. Therefore

when the maintenance function is perceived in this wider context it is

also called Physical Asset Management (PAM).

In this thesis, we will be concentrating on the maintenance

management component of asset management.


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Management has always viewed maintenance as a supporting

and non-productive function. This is due to the fact that when a

breakdown happens it is very easy to identify that it happened due to a

lack of maintenance, or wrong maintenance, on the other hand, if a

plant is performing well and generating profits, it is very difficult to

relate that to efficient maintenance. It is easy to calculate yearly

maintenance expenditure, but it is not easy to estimate the benefit of

maintenance on the return on investment or even how it can be

measured [15].

Moubray [9] classified the evolution of maintenance or

expectations of maintenance into three generations:

a. The first generation, which covers the period up to World

War I, where industry was not very highly mechanized, and

so downtime did not matter much, and prevention of

equipment failure was not a very high priority.

b. The second generation, where the pressure of World War II

increased the demand for goods of all kinds opposing to the

high drop in industrial manpower. Machines became more

complex and industry became more dependant upon them,

and as a consequence, downtime came into sharper focus.

This led to the idea that equipment failure could, and

should, be prevented and the concept of preventive

maintenance that mainly consisted of overhauls should be

carried out at fixed intervals.


- 22 -

c. Third generation, the mid seventies, where changes could

be classified under the headings of new expectations, new

research and new techniques.

Figure 3: Growing expectations of maintenance (Moubray 1997)

Maintenance is generally classified into four categories [8, 9, 16].

• Corrective maintenance (CM) - Actions carried out to

restore a defective item to a specified condition, or all

maintenance performed to correct a break down or failure

[17], this strategy started with the start of the industrial age

till before the Second World War.

• Preventive maintenance (PM) - All maintenance performed

in order to prevent a failure, or to detect a failure early, this

strategy started in the Second World War period [18].

• Predictive maintenance (PdM) - A maintenance process

based on machinery inspection, monitoring, and prediction.

Machine stops for maintenance are planned depending on

the predictions (condition-based). The terms ‘Condition

Based Maintenance’, ‘On-Condition Maintenance’, and


- 23 -

‘Predictive Maintenance’ are often used interchangeably

[19]. This strategy started in the mid seventies.

• Proactive maintenance is the application of analytical

methods, tools, and techniques to eliminate failures, extend

component life, mitigate consequences, minimize

downtimes, and optimize all resources [19, 20].

There are many maintenance policies stemming from the above

categories, some of them are:

• age replacement policy;

• random age replacement policy;

• block replacement policy;

• periodic preventive maintenance policy;

• failure limit policy;

• sequential preventive maintenance policy;

• repair cost limit policy;

• repair time limit policy;

• repair number counting policy;

• reference time policy;

• mixed age policy;

• preparedness maintenance policy;

• group maintenance policy; and

• opportunistic maintenance policy [21].

Maintenance activities exist almost everywhere there is an asset,

in transportation [22-24], in power plants [25, 26], in offshore platforms

[27], software [28], manufacturing, services and many more. These

wide ranges of businesses rely on maintenance to achieve their


- 24 -

objectives. Pressure has been placed on researchers to develop new

methodologies and techniques to accommodate the ever growing

demands and changes in business objectives, product quality, safety

and environmental requirements.

Some methodologies and techniques have been developed for

an individual industry [27, 29, 30], some have been developed for

specific industries, however the methodologies and techniques may be

modified to apply in other applications [31], while some have been

developed for use in other industries without modifications [32-34] [33].

2.3 The scope of maintenance The scope of maintenance has been defined differently by

researchers [35, 36], these differences in definitions are based on

whether the scope is for a component, equipment, system or the whole

process [15]. According to Hasting, [37] traditional maintenance has

been divided into routine and non routine maintenance. Non routine

tasks are carried out at convenience or when there is an opportunity,

or in the case of an emergency, which are mostly breakdowns.

Sometimes non routine maintenance is required even though there is

no breakdown. Figure 4 demonstrates routine and non routine types of

maintenance activities:


- 25 -

Figure 4: Routine and non routine maintenance activities (Hasting 2001)

To gain a better understanding of the different types of

maintenance methodologies that exist, we will use the classification by

Yong Sun [38].


- 26 -

Figure 5: Maintenance science classification (Sun 2006)

2.3.1 Frameworks Framework is a set of concepts linked to a planned or existing

system of methods, behaviors, functions, relationships, and objects,


- 27 -

referred to as conceptual framework. Its use in research is to outline

possible courses of action, or to present a preferred approach to a

system analysis project [39, 40]. Maintenance framework provides the

structure within which to manage the maintenance and to optimize the

life cycle of the assets in line with the business objectives of an

organization.

2.3.1.1 Reliability-Centered Maintenance (RCM) Reliability-centered Maintenance was developed over the period

of thirty years [41]. One of principal milestones in its development was

a report commissioned by the United States’ Department of Defense

from United Airlines and prepared by Stanley Nowlan and Howard

Heap in 1978. RCM was developed exclusively for the aviation

industry, in the 1980s companies started to use RCM in other

industries than aviation [42], which led to RCM II. [9].

Reliability has many definitions, Doty [43] quoted it as “the

probability of a device performing its purpose adequately for the period

of time intended under the stated operating conditions”. While

Ramakumar [44] defined system reliability as “the probability that the

system will perform its intended function for a specified interval of time

under stated condition”. To relate more to the maintenance function,

Moubray [9] definition of RCM is “a process used to determine what

must be done to ensure that any physical asset continues to do

whatever its users want it to do in its present operating context”.

The aim of RCM is to preserve the system function rather than

keeping it in service, its also a systematic approach, its methodology

develops the appropriate maintenance tactics through a thorough and

rigorous decision process [8]. Figure 6 shows the RCM process.


- 28 -

Figure 6: The RCM process (Jardine 2006)

2.3.1.2 Total Productive Maintenance A concept first introduced in Japan by Nakajima [45], after he

studied the American Preventive Maintenance, aims at building

healthier companies by strengthening people as well as equipment

[46], it is a people-centered methodology, whereby an operator’s role

is not limited to operating the equipment, but to be part of its

maintenance régime by giving them the sense of ownership. Under the

TPM concept, the operators do the inspection, effective lubrication,

reconditioning of deteriorated parts, cleaning and minor repairs, while

major overhauls and repairs are done by the maintenance crew. This

concept of involving the operator in enhancing the equipment

performance is called autonomous maintenance (AM) [47, 48].

The goals of TPM according to Wireman [49] are:

a. Improving equipment effectiveness.

b. Improving maintenance efficiency and effectiveness.

c. Early equipment management and maintenance prevention.

d. Training to improve the skills of all people involved.

e. Involving operators (occupants) in routine maintenance.


- 29 -

2.3.1.3 Business-Centered Maintenance (BCM) BCM is a conceptual maintenance process to improve equipment

effectiveness, product quality, employees’ safety and operation

performance. Kelly [50] described it as “The structure of a

methodology for developing maintenance strategy”. It is also

described as a framework of guidelines for deciding maintenance

objectives, formulating equipment life plans and plant maintenance

schedules (Maintenance planning), designing the maintenance

organization (Maintenance doing) and setting up appropriate systems

for documentation and control (maintenance control). While Hughes

[51], described it as an attitude, concept and process of continuous

improvement in maintenance and maintenance processes, equipment

condition and performance that strives to improve overall equipment

effectiveness, operations efficiency, output quality and employee

safety.

As more and more companies try to compete and survive in the

global market, the adaptation of operational concepts such as “lean

manufacturing” and “world class manufacturing practices” are

becoming commonplace. The objective of BCM therefore, is to

maximize equipment effectiveness (improve overall total efficiency) at

the minimum total cost. The elements of BCM can be summarized as:

• Asset care strategy;

• Preventive maintenance;

• Analysis and improvement;

• Planning and scheduling;

• Information management;

• Early equipment management;


- 30 -

• Training and development; and

• Maintenance facilities and tools.

2.3.1.4 Maintenance Excellence Maintenance excellence is concerned with balancing

performance, risks, and the resource inputs to achieve an optimal

solution[8], but in an industrial environment it is not so clear-cut, as it is

categorized by many uncertainties. Figure 7 shows a structural

approach to achieving maintenance excellence by Campbell [52].

Figure 7: Modified structural approach to achieving maintenance excellence by

Campbell 1995.

There are three types of goals on the route to maintenance

excellence [53]:

a. Strategic - A map that comprises the current asset

management performance level and a vision for the

performance level to be achieved must be drawn, and a


- 31 -

course must be set for the destination i.e. the asset

management strategy embraced by the organization informs

the course of action.

b. Tactical - The planning and scheduling and material

management system to control the maintenance process.

c. Continuous improvement - Also called incremental or

staircase improvement, continuous improvement is a

process or productivity improvement tool intended to have a

stable and consistent growth and improvement of all the

segments of a process or processes. Continual

improvement ensures the process stabilization and further

improvement. When an organization's growth and

development is intended, identification of all the processes

and development of a measurement analysis of each

process step is necessary [40]. To enhance the up-time of

the assets, there are two complimentary methodologies [54,

55], Total Productive Maintenance (TPM), which is a people

centered methodology, and an asset centered methodology,

which is Reliability-Centered Maintenance (RCM).

Another definition for continuous improvement is the KAIZEN

concept. The Japanese term means continuous improvement and is

taken from the words 'Kai' meaning ‘continuous’ and 'Zen' meaning

‘improvement’ [40, 56].

Maintenance Excellence may also be achieved by the mixing of

more than one strategy. Yong [38] noted that these conventional

framework are not effective due to lack of proper integration. Jonsson


- 32 -

[57] showed in a study on Swedish companies that preventive and

integrated maintenance were more important for companies seeking

competitive process control and flexibility. Wang, et al [58] proved that

an optimal maintenance strategy mix is necessary for increasing

availability and reliability levels of production facilities without a great

increase of investment, while Moudani [22] showed a mixed Dynamic

Programming approach (to cope with the fleet assignment problem)

and a heuristic technique (to solve the embedded maintenance

schedule problem). When applied to a medium charter airline, the

approach shows acceptability characteristics for operational staff, while

providing efficient solutions.

2.3.1.5 Other frameworks In recent years there has been some effort to develop or enhance

these frameworks, but they have limited applications and they are not

as widely used as the common known ones.

• The concept of e-Diagnostics and e-Maintenance is

proposed in the semiconductor industry. By using Internet

and information technologies, e-Diagnostics and e-

Maintenance [59] intend to provide equipment specialists

with the remote capabilities of connectivity, manipulation,

configuration, performance monitoring, and data collection

and analysis on equipment to achieve the goal of promptly

diagnosing, repairing, and maintaining equipment.

• The CIBOCOF framework used to develop a customized

maintenance concept in a specific company. Specific and

new to this framework is that the optimization problem of

maintenance is also taken into account. As such, models


- 33 -

described in literature finally find a way to practice, and the

gap between theory and practice lessens [60].

• The IRCMA framework, which is based on the fact that the

historical records of RCM analysis on similar items can be

referenced and used for the current RCM analysis of a new

item. Because many common or similar items may exist in

the analyzed equipment, the repeated tasks of RCM

analysis can be considerably simplified or avoided by

revising the similar cases in conducting RCM analysis [61].

• Availability Centered Maintenance (ACM) [62].

• Generic framework for integrating the maintenance

management of built-assets [63].

2.3.2 Reliability Assessment and analysis

Reliability assessment is a conceptual and quantifiable method of

highlighting whether a plant will or will not meet its intended functions

in a safe and reliable manner. The level of the reliability and safety will

depend on the size and complexity of the plant [64], when these can

be quantified from the plant specifications, the reliability assessment

becomes necessary to determine that the plant will meet its intended

functions [65].

The need for reliability assessment arises from three primary

concerns of plant users [66]:

• Economics - Organizations have long realized that even

though the cost of acquiring an asset (plant) is a fixed cost,

the cost of ownership of this asset throughout its life cycle

(LCC) can vary a lot, therefore in assessing the reliability of


- 34 -

that asset, the cost of ownership such as maintenance and

operating must be taken into consideration.

Perhaps the most important economic parameter to be

derived is the plant availability, which is the proportion of

time which a component, equipment or a system is capable

of performing its duty, whether it is running or on standby.

The cost of maintenance and operation of the plant depends

on the prediction of the availability and the behavior of this

plant [67].

• Safety - Safety assessment is to uncover all the possible

combinations of fail to danger events and the probability of

their occurrence [68]. The most important parameter is the

likelihood of certain unwanted events occurring on a plant in

a given time [66]. This will help to determine the cost of the

increased safety by assessing the required number of

additional redundancies.

• Project Viability - Reliability assessments can also provide

a comprehensive picture of the feasibility of the project to

the decision-makers regarding the return on investment,

technical feasibility, in addition to reliability and safety

issues.

Reliability assessments are carried out in the early stages of the

conceptual design stages [69, 70], while on existing plants it can be

carried out at any time [71].

2.3.2.1 Analytical models Systems usually comprise of more than one component,

sometimes numbering thousands, and in this sense equipment can


- 35 -

also be called a system. In reliability, particularly the definition system

means “Repairable” [9, 72-76]. These components are connected with

each other either by series, parallel or mixed in a complex manner.

2.3.2.1.1 Basic principles of probability based Over the years many system models have been developed which

use the principles of probability theory. Examples of these models are

the so-called “point process” or “counting process” models, which are

used for respective failure processes [77]. They are informally defined

as ‘model for randomly distributed events and having a negligible

duration. They are stochastic processes. i.e. [N (t),> 0] with state

space Z=(0,1,….). Such models like Homogeneous Poisson process

[78-80] if it has homogeneous increment.

Renewal process (RP) is another model. Renewal process is a

generalization of the Poisson process [81]. This class of processes is

used to model independent identically distributed occurrences, the

name renewal process is motivated by the fact that every time there is

an occurrence the process “starts all over again”, it renews itself [82].

It also corresponds to perfect repairs [83].

A subset model of the RP is the Generalized Renewal process or

(GRP) introduced by Yanez et al [84]. Yanez demonstrated that unlike

the normal RP, which can only be applied to the AGAN, and ABAO,

the GRP can be applied to the five states of a system after repairs.

These states are:

• as good as new; • as bad as old; • better than old but worse than new; • better than new; and • Worse than old.


- 36 -

These last three are more commonly encountered in real life.

The Weibull distribution is used extensively due to its flexibility in

modeling the three phases by the change of the shape parameter [9,

72, 85-89] and many others. Normal distribution and exponential

distributions have also been heavily exploited [90, 91].

Block Replacement models assume the items are replaced at

times, i =1, 2 … and s >0, and at failures. The preventive replacement

occur at regular predetermined intervals at a cost of c, whereas

failures within the intervals incur a cost of c + k [92-94]. An extended

block replacement policy with used item was proposed by Sheu [95].

Under such a policy, an operating system is preventively replaced by

new ones at times kT (k = 1,2,3,…) independently of the age and the

state of the system. If systems fail in [(k — 1), T, kT — δ), they are

either replaced by new ones or minimally repaired, and if in [kT — δ,

kT] they are either replaced by used ones or minimally repaired.

Sequential Preventive models was first developed for a single

unit system [96] and was first introduced by Nguyen [97], and has

been since extended to complex repairable systems [98-102].

Many other models were developed based on the basic

probability theory, discrete-time Markov renewal processes [103], The

non arithmetic estimation and consistency for renewal processes [104],

the Markov renewal approach [105, 106], some of them were

developed for the stand by or redundant systems [107-110], some are

for special cases [111-113], this is due to the fact of the complexity of


- 37 -

the mathematics involved, which makes it very hard to use for general

applications.

2.3.2.1.2 Markovian theory In 1907 a Russian mathematician, A. A. Markov (1865-1922)

introduced a special type of stochastic process whose future

probabilistic behavior is uniquely determined by its present state. That

is its behavior is non-hereditary, or without memory. A variety of

physical systems falls within this category [44].

A Markovian stochastic process with discrete state space and

discrete time space is referred to as a Markov chain. If the time (index

parameter) space is continuous, then it’s referred to as a Markov

process.

The Markov property states that only the present state gives any

information of the future behavior of the process. Knowledge of the

history of the process does not add any new information. In probability

theory, a stochastic process has the Markov property if the conditional

probability distribution of future states of the process, given the present

state and all past states, depends only upon the present state and not

on any past states, i.e. it is conditionally independent of the past states

(the path of the process) given the present state. A process with the

Markov property is usually called a Markov process, and may be

described as Markovian [40, 114]

To use the Markov modeling method in reliability a certain

assumptions has to be made [115];

a. The probabilities of a transition occurring in small time

interval ∆t from system state i to sate j is Θ ∆t, where Θ is a


- 38 -

constant. This constant takes the dimension pf the

occurrences per unit time.

b. All occurrences are independent.

c. The transition probability of more than one transition

occurrences in a small time ∆t is slight and neglected.

With these assumptions and the availability of failure rate or

repair rates of such equipment, the values of availability and

unavailability of repairable systems can be obtained [116-122].

Bukowski and Goble [123] used it for safety analysis of programmable

electronic systems, Csenki [124] used the semi Markov to model the

interval reliability of systems, Gross [125] used it for obtaining the

steady-state probability distributions of Markovian multi-echelon

repairable item inventory systems.

2.3.2.1.3 Bayesian theory Bayesian theory and Bayesian probability are named after

Thomas Bayes (1702 — 1761), who proved a special case of what is

now called Bayes' theorem. The term Bayesian, however, came into

use only around 1950, and it is not clear that Bayes would have

endorsed the very broad interpretation of probability that is associated

with his name [40].

True Bayesians actually consider conditional probabilities as

more basic than joint probabilities. It is easy to define P(A|B) without

reference to the joint probability P(A,B). This can be seen by re-

arranging the conditional probability formula to get:

P(A|B) P(B) = P(A,B) 2-1


- 39 -

but by symmetry we can also get:

P(B|A) P(A) = P(A,B) 2-2

It follows that:

2-3

Which is the so-called, Bayes Rule.

Lifetime or repairable reliability population models have one or

more unknown parameters. The classical statistical approach

considers these parameters as fixed but unknown constants to be

estimated. Hence probability statements cannot be made about the

true parameter since it is fixed, not random, since a confidence interval

for an unknown parameter is a frequency statement about the

likelihood that numbers calculated from a sample represent the true

parameter. Bayesian theory treats these population model parameters

as random, not fixed quantities.

There are some assumptions to be made prior to using the model

for reliability evaluation:

• The failure times of the system can be adequately modeled by

the exponential distribution. For a repairable systems, this

means the HPP model applies and the system is operating in

the flat portion of the bathtub curve.

• The MTBF for the system can be considered as chosen from a

prior distribution model that is an analytic representation of

previous information or judgments about the system's

reliability. The form of this prior model is the gamma

distribution (the conjugate prior for the exponential model).


- 40 -

The prior model is actually defined for λ = 1/MTBF since it is

easier to do the calculations this way.

• Prior knowledge is used to choose the gamma parameters

a, and b for the prior distribution model for λ.

Carnero [126] used it as a strategic decision for setting up of a

Predictive Maintenance Program, Guida [127] used Bayesian

procedure, to develop a model based on prior information on model-

free quantities, in order to allow technical information on the failure

process to be incorporated into the inferential procedure and to

improve the inference accuracy, El-Gohary [128] presented a three

parameters in a three state semi-Markovian reliability model with

maximum likelihood. Mazzuchi [129], presented a theoretic approach

model for determining optimal replacement strategies. The advantages

of Bayesian methodology:

• Uses prior information.

• If the prior information is encouraging, less new testing

may be needed to confirm a desired MTBF at a given

confidence.

• Confidence intervals are intervals for the (random)

MTBF.

But the disadvantages of it are:

• Prior information may not be accurate - generating

misleading conclusions

• Way of inputting prior information (choice of prior) may not

be correct

• Customers may not accept validity of prior data or

engineering judgments.


- 41 -

• There is no one "correct way" of inputting prior information

and different approaches can give different results.

• Results are not objective and do not stand by themselves.

2.3.2.1.4 Poisson process A Poisson process, named after the French mathematician

Siméon-Denis Poisson (1781 - 1840), is a stochastic process which is

defined in terms of the occurrences of events. This counting process,

given as a function of time N(t), represents the number of events since

time t=0 [40]. Poisson process is a kind of a Markov process [130] and

known for its use for modeling the number of events that are occurring

within a given time interval.

The Poisson distribution has two main applications in reliability

[72], firstly for describing the number of events (e.g. failures) in a

specified interval of time and secondly as a useful approximation for

the binomial distribution when the binomial parameter P is small, and

the Poisson based model assumes that failure probability of a system

follows the Poisson distribution and the number of failures does not

effect the failure probability and the repair does not change the

reliability of the system [131].

Perhaps the most recognized application of the Poisson process

is the Duane AMSAA (DA) model for monitoring the reliability growth

[132, 133], the model is based on the assumption that the failures are

the result of a non homogeneous Poisson process (NHPP), i.e. the

number of failures in specified time interval have a Poisson distribution

but the failure rate is non constant. Shiang [134] Huang used a non-

homogeneous Poisson process (NHPP) with a power-law intensity


- 42 -

function to present ideas on the applications of fuzzy concepts to

decision making for deteriorating repairable systems.

2.3.2.1.5 Models based on the Condition monitoring data The most preferred type of maintenance currently is the condition

based maintenance (CBM) as it is a proactive type of maintenance and

provides an early warning if the equipment condition is deteriorating

through the potential failure to failure (P-F diagram), which (depending

on the fault and type of equipment) provides an early warning for the

maintenance personnel to take the appropriate action. Preventive

maintenance can be made more efficient by periodic monitoring

wherein the state of deterioration can be assessed [135].

To increase the reliability of plants, many models have been

developed based on the availability of data through condition

monitoring techniques and equipment, Pan et al [136] developed a

support vector data description (SVDD) model which is a single

classifier and it can distinguish the normal and fault condition just using

normal samples and compared it with a neural network (ANN). Wang

[137] developed a probability model to predict the initiation point of the

second stage and the remaining life based on available condition

monitoring information. Sun [138] proposed proportional covariate

model (PCM) to overcome the difficulty of predicting the hazards of

mechanical systems accurately, and demonstrated that this new

approach to hazard estimation can reduce the number of accelerated

life tests significantly. Christer [139] introduced a replacement action

decision aid for a key furnace component subject to condition

monitoring by developing A state space model to be used to predict

the erosion condition of the inductors in an induction furnace in which a


- 43 -

measure of the conductance ratio (CR) is used to indirectly assess the

relative condition of the inductors, and to guide replacement decisions.

2.3.2.2 Other techniques 2.3.2.2.1 Condition monitoring and fault diagnosis (CMFD)

The condition monitoring and fault diagnosis CMFD is perceived

as the new generation in the practices of maintenance management.

With the Higher costs of outages of plants due to the increases in the

complexity, cost, high levels of automation and tighter profit margins

and the increased level of safety awareness have all warranted the

advances in CMFD encouraged by the technological advances in the

condition monitoring techniques [140], many techniques exist for

condition monitoring , but they all have 2 concepts in common; a

condition data acquired, interpreted and made available and an

appropriate action taking accordingly. Condition based maintenance

(CBM) or On-condition maintenance both are relying on condition

monitoring concept and techniques to initiate action [141-145].

Today there many condition monitoring techniques but they all

fall under one of these categories [9]:

• Dynamic effects

• Particle effects

• Chemical effects

• Physical effects

• Temperature effects

• Electrical effects


- 44 -

All of the CM techniques are aimed at identifying the (P-F)

interval to give an early warning so the maintenance department can

make a decision on what action should be taken and when. Some of

these techniques are [9]:

• Dynamic monitoring, such as Vibration analysis, Real time

analysis, Peak value analysis, Spike energy.

• Particle monitoring, such as ferrography, all metal debris

sensors, magnetic chip detection, graded filtration.

• Chemical monitoring, such as exhaust emission

analyzers, moisture monitor, crackle test, clear and bright

test.

• Physical effects monitoring, such as Liquid Dye

penetrates, magnetic particle inspection, ultrasonic.

• Temperature monitoring, such as infra red scanners,

temperature indicting paint.

• Electrical effects monitoring, such as power factor

testing, electrical resistance, motor circuit analysis, meggers

and other voltage generators, and many more.

2.3.2.2.2 Fault tree and root cause analysis The root cause analysis (RCA) is a problem solving method used

to find the true cause of a problem. The practice of RCA is predicated

on the belief that problems are best solved by attempting to correct or

eliminate root causes, as opposed to merely addressing the

immediately obvious symptoms. By directing corrective measures at

root causes, it is hoped that the likelihood of problem recurrence will

be minimized [40], root cause analysis is not a single, sharply-defined

methodology; there are many different tools, processes, and

philosophies of RCA in existence. However, most of these can be


- 45 -

classed into five, very-broadly defined "schools" that are named here

by their basic fields of origin: safety-based, production-based, process-

based, failure-based, and systems-based. Root cause analysis

techniques

• 5 Whys

• Barrier analysis

• Change analysis

• Causal factor tree analysis

• Failure mode and effects analysis

• Ishikawa diagram, also known as the fishbone diagram or

cause and effect diagram

• Pareto analysis

• Fault tree analysis

• Bayesian inference

Fault Tree analysis is one of the most widely-used methods in

system reliability analysis. It is a deductive procedure for determining

the various combinations of hardware and software failures, and

human errors that could result in the occurrence of specified undesired

events (referred to as top events) at the system level. A deductive

analysis begins with a general conclusion, then attempts to determine

the specific causes of this conclusion. This is often described as a "top

down" approach [146]- [73, 147, 148].

2.3.2.2.3 Reliability block diagram (RBD)

A Reliability Block Diagram is a form of reliability analysis using a

functional diagram to portray and analyze the reliability relationship of

components in a system. Each element of a system shall be

represented by a block that is in some way interconnected with or


- 46 -

through the other blocks of the system at a desired level of assembly

[149].

It can be used to facilitate the assessment of overall system

reliability, and the connections between the blocks symbolizes the way

in which the system will function as required and not necessary how

the actual physical parts are connected, that is why it is sometimes

called the success diagram method (SDM). It is considered the first

step in analyzing the system reliability [150], which looks at the logical

interdependencies (parallel or series paths) required for the system

under analysis to function correctly, and it is more suitable for

quantitative analysis as it can calculate the exact reliability of the

system at a given time [38].

2.3.2.2.4 Failure mode, effects and criticality analysis (FMECA) FMECA is a procedure that is used to analyze failures (failure

modes) and determines their effect at both the local and system

levels. The analysis can be carried out from the lowest to the highest

level of the system (bottom up), which is commonly referred to as a

hardware analysis. Alternatively, the analysis can be carried out from

the highest level to the lowest level (top down) of the system, which is

commonly referred to as a functional FMEA. The functional FMEA

considers the functional failure of components within a system.

FMEA is applied in maintenance tasks, such as reliability-

centered maintenance (RCM) and risk-based maintenance (RBM).

The effects are generally classified as operational (production),

environmental, and safety effects.

This procedure is used to plan tasks to find minimum ratio

between maintenance cost and cost due to failure effects. FMECA is a

structured method to determine equipment functions, functional


- 47 -

failures, and assess failure causes and effects [19, 42, 151]. FMEA

outputs sometimes used as inputs to a higher level fault tree analysis,

its use is limited by the time and resources available and the capacity

to derive a sufficiently detailed database [66].

Wang [152] developed a model that can be used to identify all

possible system failure events and associated causes, and to assess

the probabilities of occurrence of them particularly in those cases

where multiple state variables and feedback loops are involved by

combining FMECA and the Boolean Representation Method (BRM).

Tao et al [153] combined FMECA With fault tree analysis to asses the

reliability of a redundant actuator system.

2.3.2.2.5 Monte Carlo simulation The name “Monte Carlo'' was coined by Metropolis (inspired by

Ulam's interest in poker) during the Manhattan Project of World War II,

because of the similarity of statistical simulation to games of chance,

and because the capital of Monaco was a center for gambling and

similar pursuits [154]. Monte Carlo methods are algorithms for solving

various kinds of computational problems by using random numbers (or

more often pseudo-random numbers), as opposed to deterministic

algorithms. It can be used in reliability to evaluate the system

reliability and availability [148], where a logical model of the system

that is being analyzed is repeatedly evaluated, and each run uses

different values of the distributional parameters. Its considered an

attractive alternative approach when it comes to express the [155, 156]

complicated relationship among various parameters of the model, and

its input algorithms are easy to understand and there are no

constraints in regards to the nature of the input assumptions on the

parameters such as repair rates and failures.


- 48 -

In this study, we will be using the Monte Carlo simulation

extensively to model the system and to optimize it in conjunction with

fault tree analysis, as there were many successful researches used

this combination for system analysis and optimization [157-163], we

will also use the method in conjunction with reliability block diagrams,

as it’s a good way for representing the dependencies of the system,

reliability and availability [164-172].

2.3.3 Maintenance optimization Maintenance optimization is the discipline within operations

research concerned with maintaining a system in a manner that

maximizes profit or minimizes cost [173]. Maintenance optimization

strategies are often constructed by using the stochastic models by

concentrating on finding the optimal time or the optimal acceptable

degree of system degradation before maintenance and/or replacement is

implemented. Normally it’s done under these categories, cost based

optimization, risk based optimization, or combined optimization policy

[38].

2.3.3.1 Cost based optimization policies All maintenance activities are carried out for merely one reason,

which is to reduce or minimize the overall cost of operations, and

industrial plant availability and economics strongly depends on the

maintenance activities planned [174].

The cost-based approach to maintenance planning was originally

developed by Jardine [175], he assumed that the overhaul will return

the equipment to the as-good-as-new condition and that the failure

repair between preventive maintenance actions makes it possible to

run the machine up to the next interval (i.e., it results in a bad-as-old


- 49 -

condition). He also estimated that the optimal interval between

preventive replacements of equipment subject to breakdowns, and

may be applied to preventive maintenance.

Many attempts have been made to optimize the maintenance

tasks based on the cost factor, Kenne [176] claims to have developed

a model for the joint determination of an optimal age-dependent buffer

inventory and preventive maintenance policy in a production

environment that is subject to random machine breakdowns.

Bris [177] demonstrated an efficient simulation algorithm for the

quantification of reliability performance indicators of a complex system

that is based on Monte Carlo method by introducing a cost-

optimization problem which may be fully solved by the algorithm using

additional genetic algorithms as an applicable optimization technique,

while Barata [178] used Monte Carlo simulation to model continuously

monitored deteriorating systems and embedded the resulting model

within an ‘on condition’ maintenance optimization scheme that aims at

minimizing the expected total system cost over a given mission time.

Opportunistic maintenance is more favorable over PM, especially

in continuous operation environments as this means the equipment

does not have to go off-line in order for the maintenance task to be

performed [21, 96, 179-181].

2.3.3.2 Risk based optimization policies Conventional reliability analyses have been oriented towards

selecting the more reliable system and preoccupied with maximizing the

reliability of engineering systems [182]. Risk based optimization is taken

when the failure can endanger the life of the operator, customer,

community or the environment and linked with the losses from failures.


- 50 -

The main concerns in this field are the safety and protective

devices and stand-by equipment, and usually carried out during the

design stage [183-186].

2.3.3.3 Other Maintenance strategies A number of other strategies have been developed such as;

• Business–Based Maintenance (BBM): based on determining

the production process requirements in conjunction with

maintenance activities, and specific to emergency

maintenance, this was developed based on the business

centered maintenance (BCM) by Siemens.

• Preventive Maintenance Optimization (PMO): this strategy is

aimed at continuously reviewing and updating the

maintenance tasks based on failure history, changes in

business objectives, and the emergence of new

methodologies and techniques.

• The Relative Condition Parameter (RCP): a policy that

depends on the sophistication of condition monitoring

devices to take theses factors into account [38].

• Delay Time Modeling (DTM): this strategy optimizes

maintenance by using routine inspection and other condition

monitoring techniques to identify the time at which a defect is

originated, and understand the physics of the defect in order to

predict total failure, and therefore optimize maintenance

intervals [6].


- 51 -

2.3.3.4 Discussion The growing pressure of modern world, with the changing of the

business objectives, increased awareness of safety and environmental

concerns, the stiff competition to survive or capture a greater share

market and the modernized and complicated technology have put

maintenance on the focus to increase the reliability and availability of the

equipment and systems to meet these demands, which in turn has led to

an increasing interest in the development and implementation of optimal

maintenance models or strategies for improving system reliability,

preventing the occurrence of system failures, and reducing maintenance

costs of deteriorating systems.

Those models were classified in different types of classifications,

and the main aim of these classifications was to give the researchers

and the practitioners’ guidance, so that they can recognize the model

that best fits their maintenance needs.

Maintenance objectives in general sense is to improve system

availability and MTBF, reducing failure frequency and downtime.

However, since maintenance incurs cost, to reduce maintenance cost

is also necessary. Most researches in maintenance were aimed at

studying the stochastic behavior of systems under various

maintenance policies, and to determine the optimal maintenance

policies. The stochastic behavior of systems is mainly represented by

system maintenance cost measures, these measures are:

maintenance cost rate, discounted cost rate, and the system reliability

measures: availability, MTBF and failure frequency, etc. General

speaking, optimal system maintenance policies can be one of the

following:


- 52 -

A policy that can minimizes the system maintenance cost

rate,

A policy that can maximizes the system reliability

measures,

A policy which can minimizes system maintenance cost

rate while maintain the requirements of the system

reliability requirements to a satisfying level.

A policy which can maximize system reliability

parameters when the requirements for the system

maintenance cost are met.


- 53 -

CHAPTER THREE - FIBERBOARD PRODUCTION PROCESS 3. 3.1 Introduction

There is no precise number of how many wood pulp mills around

the world, as they are classified under different naming and categories,

or according to the type of product that they produce, but they all have

more or less the same concept of production process, a rough

estimate was done in the early nineties showed that there are about

over 1300 mills and the number is climbing. In searching the literature,

the author found very few articles about the hardboard industry [187-

190], all these articles does not address any reliability or optimization

of the system that we are going to analyze.

Some papers presented at the annual pulp and paper

maintenance and reliability conference in the US [191] annually, but all

of them do not go to specific details of the reliability and maintenance

program, rather they speak about their general experience in

implementing these programs and the achieved results.

3.2 The Hardboard production process The Masonite board production uses wood chipping mix as the

ingredient, the source of the wood chipping is from:

a. Wood saw mills waste (80%).

b. Wood logs (20%).

This percentage varies depending on availability, price and

sometimes the produced product.


- 54 -

3.2.1 Wood chipping and mixing Both forms of the wood are shipped into site and stored in the

wood yard. The logs are fed into wood chipper to transform the logs

into chips with the exact size that is suitable to produce the board. The

produced chips are then stocked in the wood yard.

The chips are then fed by front-end loader into a feeding bin

according to the mixing % indicated above. It’s then fed through series

of conveyors to chips feeding bins then through to steam heaters and

then fed to the defibrators.

3.2.2 Pulp preparation and storage stage The defibrators are machines that break the chips into their basic

fibers using steam pressure supplied from a coal fired boiler and

crushing discs called segments. The defibrators are adjusted so to

produce the required quality of wet fiber Depending on the type of

board to be produced.

Wet chips are then fed into the cyclones, which mix the fibers

with water to produce pulp, which drops into no.1 stock chest.

A sample of pulp is taken from the cyclones using a spoon like

tool; water is squeezed out of pulp and then pulled apart. Visually

determine the quality of pulp. Pulp quality for each board caliper

should meet the conditions prescribed in the comments field of the

table.

Stock chests are large concrete containers some of them are

built underground and some above ground with an agitator.


- 55 -

From stock chest no1 the pulp is transferred by pump to a

dewatering screw to drain the pulp from water and to a re-pulper, while

the water goes to Brown water chest, the pulp from the pulper is then

transferred to stock chest no 3 and from there to the Raffinators.

Stock chests are large storage containers that holds the pulp

stock, contaminated water from the process and process-recycled

water are held in, these chests are:

a. Stock chest no 1

b. Brown water chest

c. Stock chest no 3

d. White water chest

e. Stock chest no2

f. Machine chest

The Raffinators are machines that refine the pulp further than

what it is.

A sample of pulp is taken from the Raffinators using a spoon like

tool; water is squeezed out of pulp and then pulled apart. Visually

determine the quality of pulp. Pulp quality for each board caliper

should be as described above.

The pulp is then transferred to the machine chest.

3.2.3 Pulp forming stage From the machine chest the pulp transferred by pump to the

forming machine and will by circulated via the machine chest pump

until it reaches the desired consistency and then pumped by the same

pump to the former after closing the re-circulating valve. Filtered white


- 56 -

water from the process also added to the pulp as it leaves the machine

chest.

At the former the pulp is laid on fine mesh endless conveyor

called the wire to drain the pulp from water, at half the distance the

overlay pulp is introduced on top of the main pulp matt that has been

formed. The overlay is Part of the pulp that comes into the former is

drawn and used as an overlay. This pulp is laid on the main pulp

formed matt.

The drained water from the wire conveyor drops into the wire pit

and to the white water chest.

There are also 2 vacuum boxes under the wire to suck the water

from the pulp by a vacuum pump, the sucked water then transferred

into a tank and then filtered and cooled and reused to flush the lines

and as a gland water for the pumps and agitators bearings.

From the wire conveyor the pulp matt is pressed through series

of rolls to drain it from water and to give it the required bond to travel

through the line, cut into the required length and width.

3.2.4 Sizing stage After the relatively dry pulp leaves the former as a continuous

matt its sides will be trimmed by side cutters to give it the strait edge

required. The cut offs from the matt drops into a pit called the broke pit

that has an agitator to form it back to a pulp and return it to stock chest

no 2.

After the side cutter the matt is cut to the required board length

by a side cutter and by now the matt is called wet mat.


- 57 -

3.2.5 Wet mat transfer stage (plate circuit) This board travel on conveyor to another conveyor where it

reverses its direction to take it back under the first conveyor and place

it on the board plate. The board plate is a stainless steel sheet or plate

with a stainless steel mesh on top of it to help drain the residual water

from the board as it gets pressed in the press.

3.2.6 The pressing stage The plate then travels into the press loading hoist, which stacks

the plates at fixed distance on top of each other. It has 30 racks.

After the loading hoist is full there is a pusher called the wire

pusher pushes the plates into the press, the press also has 30

compartments or openings called daylights.

Each compartment comprises of wear plate, surface plate and

heating platens. Each platen has a heating medium (hot water)

charged into the platens through 2 vertical manifolds. The hot water is

at 180°C and is circulated from the boiler; this is to help initially cure

(dry) the board during the pressing process.

The pusher returns to its original position after placing the plates

(with the wet board on top) on top of the platens and the press cycle

starts.

The press is controlled by CITECT SCADA system. The cycle

starts with four hydraulic cylinders raise up pushing the platens

together, the hydraulic used is mixture of hot water and oil emulsion

and the desired pressure is generated and reached by means of the


- 58 -

accumulator, hydrauphore tank and series of low and high pressure

pumps located at the pump house.

The press has an upper firm and a lower movable press tables

both of them cast in one piece of first rate steel casting. Between the

press tables there are located 31 heating platens forming 30 openings,

into which the wet fiber boards coming from the loading hoist.

Press cycle time depends on the product and desired caliper and

can last up till 10 minutes. The pressing act drains the rest of the water

from the board, while the heat form the platens cure the board.

A more detailed explanation of the press function will be

discussed in a later section and the working pressures and

temperatures will be discussed in the products types.

After the pressing cycle is completed the press rams goes down

and the unloading hoist extract the boards out from the press. The

boards then unloaded from the board plates, the plates return to the

plate circuit for another cycle and the board travels on conveyors to the

loaders where it’s loaded in to the loading trucks and driven to the

tempering chamber.

3.2.7 Tempering stage The tempering chambers are fitted with heating coils with hot

water running through them and a fan fitted behind the coils to produce

hot air to cure the boards and to dry them.

Again the operating temps and time boards stay in the tempering

chambers depends on the product.


- 59 -

3.2.8 Humidification stage The trucks are then exit the tempering chambers and enter the

humidifying chambers where steam is introduced to the boards to bring

its moisture contents to about 7% to enhance the board properties.

Again the operating temps and time boards stay in the

humidifying chambers depends on the product.

3.2.9 Grading and secondary process

After the humidifying chambers the trucks travels to the Anthon

saws area and unloaded. Depend on the products required the boards

are either travel to the grading bins to be sent to the saws to be cut

according to the orders or they travel to the planer.

The planer is a machine that a grinds a layer from the board back

to get it to the desired thickness according to customer’s demands and

then stacked and cut to size and then strapped and sent to the

warehouse.

Figure 8: A schematic diagram of the production flow process


- 60 -

3.3 System selection Before we embark on the process of system selection, we

illustrate the process of the research. Figure 9 illustrates the research

process.

Figure 9: The research process flow chart

The first step is the system selection, as we are going to target

one area (stage) of the production line in this research. The selection

criteria will be based on the highest downtime and its criticality to the

production process.

A simple and effective method of selection is the histograms,

whereby extracting the daily downtime of the production areas or


- 61 -

stages, accumulate them and tabulate them and present them by

means of flow chart in the form of histograms.

Figure 10 shows the downtime in each area of the plant over a

period of one year. The chart indicates that the pressing plant has the

highest downtime; therefore this area will be targeted in this research.

This is in line with the fact that the press plant is the most important

part of the process, as in the press the transformation of the pulp into

board occurs, plus it represents the bottleneck of the process, which

led the company to increase the capacity of the press from twenty five

openings to thirty, this design change did increase the capacity of the

press, but still didn’t solve the bottle neck problem completely as the

problem remains with pressing cycle time.

Lost time breakdown by processes areas

0100200300400500600700800900

100011001200

Pulp

pre

p

form

er

Wet

lap

Plat

eci

rcui

t

Pres

s

Plat

e &

scre

ens

Boa

rdco

nvey

ors

Load

ers

Hea

ters

Ant

hon

saw

s

Process Area

Hou

rs

Figure 10: Breakdown of the plant areas downtime


- 62 -

3.4 An overview of the selected system (Pressing plant) The press plant is used for transforming the wet pulp into dry

board, by draining the moisture out of the pulp by means of

compression and heat. The production of wood fiber by mixing wood

chips with water is known as the wet method.

3.4.1 Press Technical Data: Press power about 4,500 Tons

Number of press cylinders 4

Hydraulic working pressure 4200psi

Diameter of press rams 700mm

Number of heating platens 31

Number of light openings 30

Size of heating platens 5700mm x 1500mm

Opening between heating platens 85mm

Active pressure surface 5580mm x 1430mm

Pressure on active press surface 782psi

3.4.2 Press technical description The press has an upper firm and a lower movable press tables,

both of them cast in one piece of steel casting. Between the press

tables there are located 31 heating platens forming 30 openings, into

which fiber boards coming from the loading hoist are introduced by

means of a charging device called the pusher.

The press originally was designed for 25 openings but extra

openings were added 6 years ago.

On pressing the movable press table is lifted by dint of four

hydraulic cylinder units, each of which being connected to the upper

press table by means of two columns.


- 63 -

The press cylinders, which are made of first rate steel casting,

are provided with exchangeable wearing liners (plates) - a feature of

great importance when its necessary to undertake a reconditioning of

the press due to wear. The packing compartment is easily accessible

by a removable gland ring and is amply dimensioned for enabling

appliance of various types of packing.

The rams sliding in the cylinders bores are made of chilled

special casting and are, therefore, very resistant both to wear and

chemical attacks. Besides the surface of the rams are grounds to high

surface finish.

The columns which owing to the heavy and varying load are

particularly strained components, are made of first- rate forging.

The heating platens are built up of solid rolled steel plates in

which a system of channels is drilled for the heat medium, which can

be steam or hot water; they have one inlet and one outlet each for the

heat medium. This necessitates only one inlet manifold and one outlet

manifold located at the ends of one of the length sides of the press at

the same time as the amount of link pipes for connection to the heating

platens is reduced to a minimum.

The pressure liquid required for the operation of the press is

mixture of hot water and emulsion oil and is obtained from a pump

plant mainly consisting of 2 low pressure pumps, 1 air loaded

accumulator and 4 high pressure pumps along with the necessary

valves and controls.


- 64 -

3.4.3 System functionality The press cycle is divided into 3 stages according to the working

pressures WP1, WP2 and WP3.

Figure 11: Schematic drawing of the press

After the loading hoist is loaded with wet boards, the press doors

which is used to protect the wet boards on the loading hoist from the

effluent leaving the boards that being pressed is opened, and the

pusher move forward inside the loading hoist and push the wet board

plates into the press. The pusher then returns to its original position

and the door is closed and the pressing cycle or WP1 starts.


- 65 -

Figure 12: Block diagram of the pressing process

• Working Pressure stage 1

The pressing cycle starts with the operation pressure

liquid first runs from the air pressurized accumulator tank

(4), the low pressure pumps (2) and the high pressure

pumps (1) (which in turn take the pressure fluid from the

hydrauphore tank) to the press cylinders, when the press

rams moves upward they squeeze the wet boards to drain

them from water, this water runs into the press sump and

discharged by the press sum pump to the water treatment

plant to be treated before releasing it into the drains.

When the press is starting to close the compression of

the fiber board is started. When the accumulator has been

discharged, it is automatically disengaged. The low

pressure pumps starts and continues to close the press, till

it reaches to its maximum pressure and is then

automatically disengaged and starts charging the

accumulator tank. Then the high pressure pumps start to


- 66 -

complete the closing of the press and developing the

working pressure up to a value of 3800 Psi.

When this has been reached , a changing over of the

high pressure pumps follows, which then goes idling, until

the pressure possibly commences to drop, when it is

automatically connected again in order to keep the pressure

at the desired value.

When the set time period for this stage ends the

pressure is released till it reaches the Working Pressure

stage 2

During the press cycle the heating platens are charged

with hot water continuously to help dry the pulp to transform

it into dry board.

• Working Pressure stage 2 The second stage starts when the press pressure

drops to 1000Psi allowing the moisture still trapped in the

board to vaporize and exits the board. By the end of this

stage the board can theoretically be exited out from the

press but it goes to a 3rd stage.

• Working Pressure stage 3 This stage is to consolidate the board strength and as

a cooling off period before the board can finally exits the

press. This happens by reversing the flow of the operating

pressure fluid back to the accumulator tank and the presses


- 67 -

drops down by its own weight opening the daylights and

have them ready to be extracted.

The boards are then extracted by a device called the

extractor, which has a gripper that grabs onto the carrier

plates tongues and pulls them into the unloading hoist,

which has the same mechanism as the loading hoist but in

reverse function.

Figure 13: Illustrates the press cycle working pressures

3.5 System boundaries definition Before start analyzing the system we need to define the

boundaries of the system, this will enable us to have a clear picture

about the components of the system and to ensure that the

uncertainties associated with the collected data are minimized.


- 68 -

Figure 14: illustrates the pressing plant and its components (Sunds defibrators-

Sweden)

We will be concentrating on the mechanical aspect of the system.

Figure 15: A schematic diagram of the system boundaries


- 69 -

Figure 14 shows an illustration of the pressing plant, while Figure

15 shows a system boundary definition.

The system consists of the following:

• Oil-water emulsion storage tank

• Low pressure pumps

• High pressure pumps

• Hydrauphore tank

• Air compressor

• Hot water boiler

• Pressing machine

• Control and flow valves


- 70 -

CHAPTER FOUR - SYSTEM RELIABILITY ASSESSMENT 4. 4.1 Introduction

After selecting the system to be targeted for the research, we will

perform an assessment of this system in order to evaluate its reliability,

its functional failures, establish the relationship between its

components, so that we can collect and process the necessary data to

build a model of the system and analyze its availability, to be able to

optimize it.

4.1.1 Functional Failure Mode, Effect and Criticality Analysis (FMECA) As mentioned earlier in the literature review, FMEA is a

procedure that is used to analyze failures (failure modes) and

determines their effect at both the local and system levels. Functional

FMEA considers the functional failure of components within a system,

its outputs sometimes used as inputs to a higher level fault tree

analysis, its use is limited by the time and resources available and the

capacity to derive a sufficiently detailed database.

The objective of a FMECA is to allow for the identification of all

the available possibilities for both catastrophic and critical failures with

all their criteria, so that they can be minimized, or eliminated, as early

as possible by the relevant preventative maintenance interventions.

At any level of sophistication, the Failure Mode, Effects and Criticality

Analysis (FMECA) have to contribute significantly to the decision

making process regarding the maintenance program that is being

implemented.

The recommended action will be added after optimizing the

system and selecting the optimal strategies.


- 71 -

Appendix 12 shows the functional FMECA for the plant

4.1.2 Modeling the failure data The objective of reliability data analysis is to construct a

probability model of the failure process. The main reason for building

such models is that they offer scope for predicting and improving the

future reliability performance of equipment or systems. These results

can be very beneficial for assuring the safety and productivity of

installations, and provide input to other associated studies in

maintenance planning, inspection scheduling, spares holdings and

many other activities where the reliability performance of plant,

systems and equipment is of concern [72].

Having defined the system boundaries, we will model the failure

data and fit them into the appropriate distribution and estimate the

reliability parameters.

Calculating the Correlation Coefficient:

The correlation coefficient is a measure on how well the linear

model fits the data. The closer the absolute value is to 1, the better the

fit.

The correlation coefficient is calculated in the Cumulative

Probability distribution (cpd) of every graph by the Weibull program

and is denoted by the symbol (ρ).

The correlation coefficient is calculated from


- 72 -

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛⎟⎠

⎞⎜⎝

⎛

−

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛⎟⎠

⎞⎜⎝

⎛

−

−=

∑∑

∑∑

∑∑ ∑

=

=

=

−

=

= =

N

i

N

ii

i

N

i

N

ii

i

N

i

N

i

N

iii

ii

N

yy

N

xx

N

yxyx

1

2

12

1

2

12

1

1 1.

ρ 4.1

Where ix and y are the x and y values of points in the

cumulative probability plot. N is the total number of points plotted.

Estimating the Goodness Of Fit (GOF):

Goodness of fit is a measure of how well a statistical

model fits a set of observations. Measures of goodness of fit

typically summarize the discrepancy between observed

values and the values expected.

The goodness of fit (ε) is calculated in the Cumulative

Probability distribution (cpd) of every graph by the Weibull

program and is denoted by the symbol (ε).

The expression below indicates how the goodness of fit indicator

is calculated.

( )2

1

ˆ

N

yyN

iii∑

=

−=ε 4.2


- 73 -

Where iy and iy are the fitted Weibull unreliability values and

estimated unreliability point values respectively. N is the total number

of points plotted.

Also calculated and denoted by B10, B15, and B20, is the life

where by 10%, 15%, and 20% of component failures would have

respectively occurred. i.e., the times, at which the Unreliability of the

component is 0.1, 0.15, and 0.20 respectively.

Note:

• All the parameters estimated by the Weibull distribution model,

used Median Rank method for estimating these parameters,

unless specified.

• To model the data we will use the Isograph software.

4.1.2.1 Press Rams 4.1.2.1.1 Estimating the MTBF

We obtain the failure data from the downtime database, since the

rams seals are replaced every time they fail, hence it can be

considered complete data.

Table no.1 shows the failure data taken from the database. Table 1: Press rams failure data


- 74 -

Re-arranging the data and calculating the times to failure (TTF) in

days.

Table 2: Press rams TTF (days)

Plotting the times to failure on Weibull graph and estimating the

reliability data.

Figure 16: Press rams Weibull plot

Also plotting the data and estimate the mean life

From the graphs we get the followings:

µ=390.9 days

η=453.1 days


- 75 -

β=2.0

The cdf (cumulative density function) at mean life; β

η)(

)( 1t

t eF−

−= 4.3

2)1.4539.590(

)9.590( 1−

−= eF

F(t)=0.52

R(t)=1-F(t)=1-0.52 4.4

R(t)=0.48

MTBF=η[(1+β)/β] 4.5

MTBF=680 days

4.1.2.1.2 Estimating the Mean Time To Repair MTTR MTTR is a characteristic describes the average time to repair a

system with maintainability M(t) and is generally the term used to

represent the average restoration time in quantitative analysis.

Since the repair mode is repacking the ram seals (single repair

mode), therefore we can calculate the MTTR by dividing the total time

taken by no. of repairs.

Table 3: Press rams Repair times (hrs)

repairsofnotakentimetotalMTTR

....

= 4.6

MTTR = 14.6/10 = 1.4 hrs

4.1680680)(+

=+

=MTTRMTBF

MTBFAtyAvailabili 4.7

A=0.997


- 76 -

4.1.2.2 Solenoid valves S1, S2, S3, S4 and S10 4.1.2.2.1 Estimating the MTTF

Since there are no available data for these components, we will

make use the generic reliability data available from the reliability data

handbook[72].

From the generic tables, the solenoids median failure rate is:

)/(7.5.. mhfratefailureMedian =

66 107.5

107.5 −== xλ

tetR λ−=)( 4.7

)8760107.5( 6

)8760( xxeR−−=

951.0)( =tR

)(1)( tRtF −=

049.0951.01)( =−=tF

For non-repairable components

)()( tFtQ = 4.8

lityUnavailabitQWhere =)(.

And;

)(1)( tQtA −= 4.9

A (8760) =1-0.049=0.951

4.1.2.3 Low-pressure pumps 4.1.2.3.1 Estimating the MTBF

We test the data to see if it fits the HPP


- 77 -

Table 4: Low pressure pumps TTF

Rearranging the failure data to plot the cumulative failures:

Table 5: low pressure pumps Ranked ttf,N(T),log cumulative and log N(T)

Since the graph is a curve, its not HPP

Figure 17: Testing the LPPs data against the HPP


- 78 -

We test the data against the NHPP by taking the log

Figure 18: Low pressure pumps log TTF-log N(T)

Since it’s a NHPP, we use DA-AMSSA model to determine the

model parameters

The failure intensity function is given by: 1)( −= βλβ ttw 4.10

Where β=

117.3008.1

10log1470log1log12log

−−

=−−

β= 0.5

βλtN

= 4.11

497.0147012

=

λ=0.312

dayfxxxtw /1006.414705.0312.0)( 315.01470

−− ==

= 4.06x10³ f/day


- 79 -

MTBF=1/ w(t) 4.12

= 5898 hrs

The instantaneous failure rates refers to the population of 2

pumps, therefore at 1470 days, the MTBF is

MTBF= 2x 5898=11796 hrs

))(

(1)( xM

t

x etF−

−= 4.13

)117961470(

)( 11470

−−= eF t

117.0)( 1470=tF

R(t)=1-F(t)

R(t)=1-0.11=0.883

4.1.2.3.2 Estimating the MTTR Rearranging the repair data to plot the repair model and truncate

them Table 6: Low pressure pumps repair data


- 80 -

We plot the repair data and obtain the MTTR

Figure 19: Low pressure pumps repair distribution graph

For lognormal distribution

2

21σµ +

= eMTTR 4.14

From the graph, µ= 21.9353 and σ= 149.331

MTTR=9.32 hrs

32.91179611796)(

+=

+=

MTTRMTBFMTBFAtyAvailabili

A=0.9992

4.1.2.4 High-pressure pumps 1-4 4.1.2.4.1 Estimating the failure model

Processing the data and checking the process against HPP


- 81 -

Table 7: HPPs re-arranged failure data

High pressure pumps

0

5

10

15

20

25

30

0 500 1000 1500TTF

N(T

)

Figure 20: Testing the High Pressure Pumps 1-4 data against the HPP

Since it’s a curve, we test for NHPP, by taking log of the values

Figure 21: HPPs 1-4 log failure data

Using DA-AMSSA model, we determine the model parameters


- 82 -

1)( −= βλβttw

Where

46log227log1log15log1

−−

=β

66.136.2018.1

−−

=

β1= 1.68


−−

=β

4.218.32.136.1

−−

=

β2= 0.2

βλtN

= 4.15

68.1227151 =λ

λ1=0.0016

2.01500232 =λ

λ2=5.32 The estimated instantaneous failure rates at 251 and 1500 days

are: 168.1

227 22768.10016.0)( −= xxtw

= 0.1 f/day

MTBF=1/ w(t)x24

=223.2 hrs 120.0

1500 150020.032.5)( −= xxtw

= 0.003 f/day


- 83 -

MTBF=1/ w(t)x24

= 7824 hrs


pumps, therefore at 227 days, the MTBF is

MTBF= 4x 223.2=892.8 hrs

And at 1500 days

MTBF=4x7825=31296 hrs,

Because this is the last value, therefore we will use this value in

the analysis

)312961500(

)( 11500

−−= eF t

046.0)( 1500=tF

R(t)=1-0.04=0.953

4.1.2.4.2 Estimating the MTTR

Re-arranging the times to repair and truncate them

Table 8: HPPs1-4 times to repair


- 84 -

Using the exponential distribution to estimate the MTTR

Figure 22: HPPs 1-4 repair distribution graph

MTTR=1/µ 4.16

=1/0.52

=1.9 hrs

9.13129631296)(

+=

+=


A=0.999

4.1.2.5 Boiler set 4.1.2.5.1 Estimating the MTBF

Checking the data against HPP


- 85 -

Table 9: Bolier TBF (hrs) /N(T)

Boi l e r c umul a t i v e f a i l ur e a ga i nst e l a pse d t i me

-5

0

5

10

15

20

25

30

0 1000 2000 3000 4000 5000 6000 7000 8000

Elapsed t ime (t )

Figure 23: Boiler TBF/N(T) graph

Since the graph is curved, its not HPP, and therefore we will

check the data against NHPP

Taking the log of cumulative failures N(T) and the log of TBF and

plotting them


- 86 -

Table 10: Bolier log N(T), log TBF

B o iler set reliab il it y g rowt h mod el

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.86 1.96 2.06 2.16 2.26 2.36 2.46 2.56 2.66 2.76 2.86 2.96 3.06 3.16 3.26 3.36 3.46 3.56 3.66 3.76 3.86 3.96

Log t

Figure 24: Bolier log t-log N(T)

As the plot is straight line, hence it’s a NHPP

Using DA-AMSSA model, we determine the model parameters 1)( −= βλβttw


- 87 -

Where β1=

68.19.2015.1


−−

=−−

=β

β1= 1.1

06.383.318.141.1


−−

=−−

=β

β2= 0.3

βλtN

=

1.1792141 =λ

λ1=0.009

3.06792262 =λ

λ2=1.84

The estimated instantaneous failure rates at 792 and 6792 days

are: 11.1

792 7921.1009.0)( −= xxtw

= 0.019 f/day

MTBF=1/ w(t)x24

=1263 hrs 13.0

6792 679230.084.1)( −= xxtw

= 0.001 f/day

MTBF=1/ w(t)x24

MTBF=24000 hrs

)240006792(

)( 16792

−−= eF t

247.0)( 6792=tF


- 88 -

R(t)=1-0.24=0.753

4.1.2.5.2 Estimating the MTTR Re-arranging the repair data and truncating them

Table 11: Boiler repair data

Plotting the data using the exponential distribution model

From the plot, µ is 1.89654

MTTR=1/µ

=1/1.89654

MTTR=0.52 hrs


- 89 -

Figure 25: Boiler repair model graph

52.02400024000)(

+=

+=


A=0.9999

4.1.2.6 Pre-fill and exhaust valve no.6 4.1.2.6.1 Estimating failure model MTBF

Re-arranging the data and truncate them

Table 12: valve no.6 TTF and TTR


- 90 -

Plotting the failure data using, Weibull distribution and estimating

the failure model parameters.

Figure 26: Valve no.6 Weibull distribution graph

For Weibull distribution, the

MTBF=η[(1+Β)/Β]

Where,η= 16735.2 hrs

Β = 0.88

MTBF=16735.2[(1+0.88)/0.88]

MTBF=35752.47 hrs

47.357528760

1)8760(−

−= eF

F(8760)=0.217

R(t)=1-0.217=0.782

4.1.2.6.2 Estimating repair model MTTR Plotting the repair data using the exponential distribution


- 91 -

Figure 27: V/v no.6 exponential distribution repair graph

MTTR= 1/µ, where µ=2.23333

= 1/2.23333 MTTR= 0.44 hr

44.047.3575247.35752)(+

=+

=MTTRMTBF

MTBFAtyAvailabili

A=0.999

4.1.2.7 Blocking magnet 4.1.2.7.1 Estimating failure model parameters

Since the solenoid is replaced every time it fails, therefore the

failure data can be considered as complete data.

Table 13: Blocking magnet TTF


- 92 -

Figure 28: Blocking magnet failure graph

From the distribution we obtain,

η = 11430 hrs

β =0.49

4.17 51.0

1143008760

1143049.0)8760(

−

⎟⎠⎞

⎜⎝⎛ −

=λ

5108.4 −= xλ

MTTF=1/ λ

=20788.5hrs tetR λ−=)(


- 93 -

)8760108.4( 5

)8760( xxeR−−=

656.0)( =tR

656.01)(1)( −=−= tRtF

344.0)( =tF

)()( tFtQ =

1)()( =+ tQtA

A=1-0.344

A=0.656

4.1.2.8 High-pressure pump no. 5

The failure parameters are supplied by the manufacturer-

Hammellmann GMBH – Germany. The manufacturer indicated that

this pump exhibits constant failure rate.

MTBF= 25000 hrs

3504.025000

11===

MTBFλ 4.18

)(1)( MTBF

t

etF−

−= 4.19

)250008760(

1)8760(−

−= eF

296.0704.01)8760( =−=F

704.0)8760( =R

5.22500025000)(

+=

+=


MTTR= 2.5 hrs

A=0.999


- 94 -

4.1.2.9 Non-return valves There are no data exist for these valves, therefore we will make

use of the generic data available from the reliability data handbook

[72]. 61056.4 −= xλ

MTBF=1/λ 4.20

=219298 hrs

Assuming a constant failure rates from similar equipment

distribution

]8760).1056.4[( 06

)8760(−−= xeR

961.0)8760( =R

039.0961.01)8760( =−=F

)()( tFtQ =

1)()( =+ tQtA

A=1-0.039

A=0.961

4.1.2.10 Thruster valves 1- 4 4.1.2.10.1 Estimating the MTBF

Re-arranging the data from the lost time database and truncate

them

Table 14: Thruster valves TBFs


- 95 -

Since the valves are repairable, we check them against HPP

Table 15: Thruster valves TBFs against N(T)

We plot the data to verify the model

Thruster valves

02468

10121416

0 500 1000 1500 2000

TBF

N(T

)

Figure 29: Thruster valves TBF against N(T)

Since its curve, we will test it against NHPP, by taking the log of

the values and plotting them


- 96 -

Table 16: Thruster valves log TBF against log N(T)

Figure 30: Thruster valves log TBF-log N(T)

As the plot is straight line, hence it’s a NHPP

Using DA-AMSSA model, we determine the model parameters 1)( −= βλβ ttw

Where

4.117.209.0


−−

=−−

=β


- 97 -

β1= 1.16

6.225.395.015.1


−−

=−−

=β

β2= 0.3

βλtN

=

16.114981 =λ

λ1=0.024

3.01764142 =λ

λ2=1.48 The estimated instantaneous failure rates at 149 and 1764 days

are:

dayfxxtw /006.01496.1024.0)( 116.1149 == −

= 0.06 f/day

MTBF=1/ w(t)x24

=400 hrs

dayfxxtw /0023.017643.048.1)( 13.01764 == −

MTBF=1/ w(t)x24=10435 hrs,


thrusters, therefore at 1764 days, the MTBF is

MTBF =10435x4=41739hrs

)417398760(

)( 1)8760(1764

−−= eF t

189.0)8760()( 1764=tF

R(t)=1-0.189=0.810


- 98 -

4.1.2.10.2 Estimating the MTTR Re-arranging the repair times data

Table 17: Thruster valves repair data

Plotting the data using the log- normal distribution

Figure 31: Thruster valves log-normal repair model plot

From figure 33, µ = 0.472 and σ = 0.53

2

21σµ+

= eMTTR

MTTR= 1.8 hrs

8.14173941739)(

+=

+=


A=0.9998


- 99 -

4.1.2.11 Tubular guides 4.1.2.11.1 Estimating MTBF

Re-arranging the data and verify the model against HPP

Table 18: Tubular guides’ failure data

Table 19: Tubular guides TBF-N(T)

Plotting the data against N(T)


- 100 -

Tubular guides

0

5

10

15

20

25

30

35

0 200 400 600 800 1000 1200

TBF

N(T

)

Figure 32: Tubular guides TBF-N(T)

Since it is not a straight line, hence its not a HPP.

We take the log of the data and plot them to check against NHPP

Table 20: Tubular guides Log TBF-Log N(T)

Plotting the data to estimate the process and its parameters


- 101 -

Figure 33: Tubular guides log TBF-log N(T)

1)( −= βλβttw

78.077.1008.1

6log59log1log12log1

−−

=−−

=β

β1= 1.1

77.118.211.132.1


−−

=−−

=β

β2= 0.512

18.201.334.149.1


−−

=−−

=β

β3= 0.08

βλtN

=

1.159121 =λ

λ1=0.13

51.0151212 =λ


- 102 -

λ2=1.62

08.01017313 =λ

λ2=17.8

The estimated instantaneous failure rates at 59, 151 and 1017

days are:

dayfxxtw /21.0591.131.0)( 11.159 == −

MTBF=1/ w(t)x24

= 114.2 hrs

dayfxxtw /07.0151512.062.1)( 1512.0151 == −

= 0.07 f/day

MTBF=1/ w(t)x24

= 342 hrs

dayfxxtw /02.0101708.08.17)( 108.01017 == −

MTBF=1/ w(t)x24

MTBF=9850 hrs

)98501017(

)( 11017

−−= eF t

099.0)( 121=tF

901.0)( 121=tR

4.1.2.11.2 Estimating MTTR We arrange the data and truncate them, and then we plot them

using the log- normal distribution plot.


- 103 -

Table 21: Tubular guides repair data

Figure 34: tubular guides Log-normal plot of repair data

From the graph, µ=0.21, and σ=0.2

2

21σµ+

= eMTTR


- 104 -

MTTR=1.25hrs

25.198509850)(+

=+

=MTTRMTBF

MTBFAtyAvailabili

A=0.9998

4.1.2.12 Heating platens leaks 4.1.2.12.1 Estimating the MTBF

Taking failure data for 3 year (26280 hrs) as shown in table 22

Table 22: Heating platens failure data

failuresofno

timespairfailureXreofnoplatensofhrsXnoMTBF..

)...()..26280( −= 4.21

18)25.018()3026280( XXMTBF −

=

MTBF=43799hrs

)(1)( MTBF

T

eTF−

−=

)437998760(

1)8760(−

−= eF

F (8760) =0.182

R (8760) =0.828


- 105 -

4.1.2.12.2 Estimating the MTTR Re-arranging the data and plotting them using the exponential

distribution

Table 23: Heating platens repair data

Figure 35: Heating platen exponential repair model

MTTR=1/µ

From the repair time model graph;

µ=3.218

MTTR=0.31hrs

31.04379943799)(

+=

+=


A=0.999


- 106 -

4.1.2.13 Link pipes 4.1.2.13.1 Estimating the MTBF


Table 24: Link pipes failure data

Since the pipes are repairable, hence;

failuresofnotimespairfailureXreofnopipesofhrsXnoMTBF

..)...()..8760( −

=

32)25.032()608760( XXMTBF −

=

MTBF=131400hrs

)(1)( MTBF

T

eTF−

−=

)131400

8760(1)8760(

−−= eF

F (8760) =0.065

R (8760) =0.935


- 107 -


Table 25: Link pipes ranked repair times

Plotting the repair times using log-normal distribution

From the graph, µ=0.244, and σ=0.32

2

21σµ+

= eMTTR

MTTR=0.74hrs

74.0131400131400)(

+=

+=


A=0.999


- 108 -

Figure 36: Link pipes repair graph

4.1.2.14 Bleeding valve no.7 4.1.2.14.1 Estimating the MTTF

From the generic data handbook

Median rank=11 f/mh 5101.1 −= xλ

MTTF=1/λ

MTTF=90909hrs tetR λ−=)(

8760101.1 5

)8760( XxeR−−=

R(8760)=0.908

1)()( =+ tFtR

F(8760)=1-0.908

F(8760)=0.092

)()( tFtQ =

1)()( =+ tQtA


- 109 -

A=1-0.092

A=0.908

4.1.2.15 Temperature sensor From the generic data handbook

Median rank=6.8 f/mh

=0.0595f/yr 6108.6 −= xλ

MTTF=1/λ

=147058hrs tetR λ−=)(

8760108.6 6

)8760( XxeR−−=

R(8760)=0.942

1)()( =+ tFtR

F(8760)=1-0.94

F(8760)=0.058

)()( tFtQ =

1)()( =+ tQtA

A=1-0.06

A=0.942

4.1.2.16 Exhaust fan bearings



=0.998 f/yr

λ=1.14x10-5

MTTF=87719.29hrs tetR λ−=)(


- 110 -

87601014.1 5

)8760( XxeR−−=

R(8760)=0.904

1)()( =+ tFtR

F(8760)=1-0.904

F(8760)=0.096

)()( tFtQ =

1)()( =+ tQtA

A=1-0.096

A=0.904

4.1.2.17 Pressure sensor


Median rank=28 f/mh 5108.2 −= xλ

MTTF=35714.28hr tetR λ−=)(

8760108.2 5

)8760( XxeR−−=

R(8760)=0.782

1)()( =+ tFtR

F(8760)=1-0.782

F(8760)=0.218

)()( tFtQ =

1)()( =+ tQtA

A=1-0.22

A=0.782


- 111 -

4.1.2.18 Timer From the generic data handbook

6102.1 −= xλ

MTTF=833333hrs tetR λ−=)(

8760102.1 6

)8760( XxeR−−=

R(8760)=0.989

1)()( =+ tFtR

F(8760)=1-0.989

F(8760)=0.011

)()( tFtQ =

1)()( =+ tQtA

A=1-0.01

A=0.989

4.1.2.19 Hydrauphore tank 4.1.2.19.1 Estimating the MTBF

From the generic failure data, we will model the failure rate using

the Design, Operating, Environment (DOE) method. In 1996 Andres

and Moss [192] proposed this model for calculating stress factors for

rotating machinery. This basic model has now been modified for use

with process equipment. The basis of the model is the adoption of a

reference point (generally the median of the range of recorded generic

failure rates for similar equipment.) to define a reference equipment

and base failure rate [72]

∏= iAbXA Kαλλ 4.22

Where;


- 112 -

XAλ = Predicted failure rate for equipment X.

bλ = Best estimate of a mean failure rate for similar equipment to

X

aα =Proportion of failures (=1, if all failure modes are included)

∏ iK =Product of various "k" factors, xiK 2=

Assumptions:

* All failure modes are considered (i.e. αa=1)

* The equipment is operating in a typical industrial

environment (non corrosive onshore plant).

* A log normal relationship between equipment failure

rates and operating conditions.

Figure 37: Log-normal distribution of centrifugal pump and circuit breaker failure rates

(independent samples)(T.R.Moss) [72]


- 113 -

Fig 37 shows the log-normal distribution of observed centrifugal

pump & circuit breaker failure rates, the evidence gives credence to

the adoption of the simple failure rate prediction model used, where

the generic failure rates range is scaled logarithmically with the general

format.

Method: First we obtain generic data from different sources to get the best

estimate.

Table 26: Generic data from different sources

Taking geometric mean to obtain the range best estimate failure

rate

Hence, the ordered failure rates estimate is:

Table 27: Ordered failure rates data estimates

Based on the median rank, the Best estimate λ = 16.5 f/mh


- 114 -

We obtain the K values from stress ranking table

Design attributes: Table 28: Hydrauphore tank design attributes

Mean design weight=(0+(-1.5)+0+(-1)/4=-0.625

k1=2

=1.54

Operating attribute: Table 29: Operating attributes

Weight difference = -1/4 = -0.25 )25.0(22 −=K

=0.84

Environment attributes:

Table 30: Environment attributes


- 115 -

)25.0(22 =K =1.19

πki=1.54 x 0.84 x 1.19=1.539

λXA=1.9 x 1.0 x 1.539=2.924 failures/mh 0610924.2 −= xλ

MTBF=1/λ

MTBF=341997h

Assuming a constant failure rate from similar equipment tetR λ−=)(

]8760).0413.0[()8760( −= eR

974.0)8760( =R

025.0974.01)8760( =−=F


Based on information provided by engineering staff

MTTR= 3 hrs

33419934199)(

+=

+=


A=0.999

4.1.2.20 Valve no.11 4.1.2.20.1 Estimating the MTBF

Shift fitter fail to open valve after shutdown (human error)

From generic failure rate tables for single operation task


λ=5.50E-06

MTBF=1.82E+05 tetR λ−=)(


- 116 -

]8760).105.5[( 06

)8760(−−= xeR

952.0)8760( =R

048.0952.01)8760( =−=F

Since this is unrevealed failure,

Hence, the probability of being in the failed state in 2 weeks time

(say 336 hrs) is the Fractional Dead Time FDT:

T: inspection interval=336 FDT=λΤ/2 4.23

9.24E-04

Unavailability (U) =FDT

U+A=1 4.24

A=1-9.24E-04

A=0.999


Based on information provided by engineering staff,

MTTR= 2.0 hrs

4.1.2.21 Air compressor 4.1.2.21.1 Estimating the MTBF

From the generic failure data, we model the failure rate using

DOE method

∏= iAbXA Kαλλ Where;

λXA= Predicted failure rate for equipment X.

λb = Best estimate of a mean failure rate for similar equipment to

X

αa = Proportion of failures (=1, if all failure modes are included)


- 117 -

πki = Product of various "k" factors, Ki=2х

* All failure modes are considered (i.e. αa=1)

* Typical reciprocating air compressor operating in

industrial environment

Design attributes: Table 31: Design attributes

)5.2(21 −=K 0.18

Operating attribute: Weight difference=-1

)5.0(22 =K =0.5

Environment attributes: Weight difference=-1

)5.0(22 =K

=0.5

πki=0.18 x 0.5 x 0.5=0.045

λXA=4 x 1.0 x 0.045=0.18 failures/mh 07108.1 −= xλ

07108.111

−==x

MTBFλ

]8760).108.1[( 07

)8760(−−= xeR

998.0)8760( =R


- 118 -

002.0998.01)8760( =−=F

MTBF=5555555.5h


Based on information provided by engineering staff,

MTTR= 0.5 hrs

5.05.55555555.5555555)(+

=+

=MTTRMTBF

MTBFAtyAvailabili

A=0.999

4.1.2.22 Control valves no.8 & 9

From the generic failure data, we model the failure rate using

DOE method

∏= iAbXA Kαλλ Where;

λXA=Predicted failure rate for equipment X.

λb= Best estimate of a mean failure rate for similar equipment to

X

αa=Proportion of failures (=1, if all failure modes are included)

πki=Product of various "k" factors, Ki=2х

Assumptions: * All failure modes are considered (i.e. αa=1)

* The control valves operating in a typical industrial

environment

* a log relationship between equipment failure rates and

operating conditions

Method: First we obtain generic data from different sources to get the best

estimate


- 119 -

Table 32: Generic data from different sources

Taking geometric mean to obtain the range best estimate failure

rate

√ 0.4 x80=5.656 f/mh

√ 4.56 x 79.9=19.087 f/mh

Hence, the ordered failure rates estimate is

Table 33: Ordered failure rates estimates

Based on the median rank, the Best estimate λ = 19.087 F/mh

We obtain the K values from stress ranking table


- 120 -

Design attributes: Table 34: Design attributes

Operating attribute:

Table 35: Operating attributes

)0(22 =K

=1.00

Environment attributes:

Table 36: Environment attributes

Weight difference= (1+0+0+1)/4

)5.0(22 =K

= 1.414

πki=1 x 1 x 1.414=1.414

λXA=19.087 x 1.0 x 1.414=27.0 failures/mh


- 121 -

0506 107.21027 −− == xxλ

]8760).1027[( 05

)8760(−−= xeR

789.0)8760( =R

211.0789.01)8760( =−=F

MTBF=1/λ

MTBF=370374h

MTTR=1 hrs

13703737037)(

+=

+=


A=0.999

4.1.2.23 Change over valve no.5

From tables=4.7 f/mh

λ=0.0000047

MTBF=1/ λ=1/0.0000047

MTBF=212766

2127668760

1)8760(−

−= eF

041.0)8760( =F

959.004.01)8760( =−=R

MTBF=12516h

MTTR=3hrs

3212766212766)(

+=

+=


A=0.999

4.1.2.24 PLC From tables=29.7 f/mh


- 122 -

λ=2.97X10-5

]8760).1097.2[( 05

)8760(−−= xeR

77.0)8760( =R

23.0771.01)8760( =−=F

MTBF=33670h

MTTR=1.5hrs

5.13367033670)(

+=

+=


A=0.999

4.1.2.25 Piping system From generic tables

Median failure rate=1.24e-4 f/mh

λ=2.19x10-8

]8760).1019.2[( 08

)8760(−−= xeR

9998.0)8760( =R

0002.0998.01)8760( =−=F

MTBF=45662100h

MTTR=1.0hr

0.14566245662)(

+=

+=


A=0.999

4.1.2.26 Platens off position 4.1.2.26.1 Estimating the MTBF



- 123 -

Table 37: Platens’ failure data

Since the platens are repairable, hence;

failuresofnotimespairfailureXreofnoplatensofhrsXnoMTBF

..)...()..8760( −

=

18)37.118()308760( XXMTBF −

=

MTBF=14598hrs

)(1)( MTBF

T

eTF−

−=

)145988760(

1)8760(−

−= eF

F (8760) =0.452

R (8760) =0.548

4.1.2.26.2 Estimating the MTTR Censoring the repair data, rank them and plot them on

exponential distribution


- 124 -

Table 38: Platens’ off-position repair data

nt

MTTR iΣ= 4.25

hrsMTTR 37.118

8.24==

37.11459814598)(

+=

+=


A=0.999

4.1.2.27 Hydrauphore relief valve From generic data tables

05103.2 −= xλ

]8760).103.2[( 05

)8760(−−= xeR

817.0)8760( =R


- 125 -

183.0817.01)8760( =−=F

MTTF=43478.2 hrs

MTTR=1hrs

0.12.434782.43478)(+

=+

=MTTRMTBF

MTBFAtyAvailabili

A=0.999

4.1.2.28 Hydraulic storage tank parameters

From generic data tables

Failure rate=0.15f/mh 07107.5 −= xλ

MTBF=1/λ=1754386hrs

MTTR=3hrs

]8760).107.5[( 07

)8760(−−= xeR

995.0)8760( =R

005.0995.01)8760( =−=F

317543861754386)(

+=

+=


A=0.999

Appendix 1 shows the system components calculated

Unreliability, Reliability and Availability.

4.1.3 Fault Tree Analysis (FTA) Fault tree diagrams are logic block diagrams that display the

state of a system (top event) in terms of the states of its components

(basic events). Fault tree diagrams are a graphical design technique.

An FTD is built top-down and in term of events rather than blocks. It

uses a graphic "model" of the pathways within a system that can lead


- 126 -

to a foreseeable, undesirable loss event (or a failure). The pathways

interconnect contributory events and conditions, using standard logic

symbols (AND, OR etc). The basic constructs in a fault tree diagram

are gates and events.

The main purpose of the fault tree analysis is to evaluate the

probability of the top event by using the analytical or the statistical

methods. These calculations involve system quantitative reliability and

maintainability information, such as failure probability, failure rate, or

repair rate. FTA can provide useful information concerning the

likelihood of a failure and the means by which such a failure could

occur. Efforts to improve system safety and reliability can be focused

and refined using the results of the FTA.


- 127 -

4.1.3.1 Constructing the fault tree model


- 128 -


- 129 -


- 130 -


- 131 -

4.1.3.2 Estimating the system probability of failure from FTA a) Not operating safely

The Blocking magnets system S7, S8 and S9 is operating on 2

out of 3 bases, therefore the probability of failure is: 23 3)( PFFtFs += 4.26

23/ 35.065.0335.0)( xxtF MGNTB +=

27.0)(/ =tF MGNTB

)}](1{)}(1[{1)( /// tFxtFtF MGNTBFANEXHOPSS −−−=

}]27.01{}1.01[{1/ −−−= xF opsS

343.0/ =opsSF

b) Insufficient Pressing Pressure (IPP) gate


- 132 -

• High Pressure Pump assembly no. 1 gate

)}](1)}.{(1)}.{(1[{1)( / tFtFtFtF vthrsutervnrvhpphppassy −−−−=

4.27

256.0}]189.01{}039.01{}046.01[{1)( =−−−−= xxtFhppassy

744.0256.011 =−=−= hppassyhppassy FR

And;

= HPP assy no.2, HPP assy no.3, HPP assy no.4

• High Pressure Pumps (1-4) assemblies ¾ voting gate 2234

41 64)( FPPFFtFhpps ++=− 4.28

235.0

)236.0(764.06)236.0(764.04)236.0()( 223441

=

++=− xxxxtFhpps

• High Pressure Pumps (1-4) and 5 Assembly gate

069.0296.0235.0)().51( ==− xtF assyHPP .

• Pressure reducing valve 7 assembly gate

)}](1)}.{(1[{1)( 7#/4).7/( tFtFtF avvSassyavv −−−=

136.0}]092.01{}049.01[{1)().7/( =−−−= xtF assyavv

And;

= V/V no.7b assembly

=)(7#/ tF ASSYVV )}]()}.{([{1 .7/).7/( tFtF assybvvassyavv− ).

253.0}]136.01{}136.01[{1)(7#/ =−−−= xtF ASSYVV

)}](1.{)}(1)}.{(1)}.{(1.{

)}(1)}.{(1)}.{(1[{1)(

)51(

7#/.//

.6#/

tFtFtFtF

tFtFtFtF

assyHPP

ASSYVVNRVLISNSRP

syspipingtimervvIPP

−−−−−

−−−−=


- 133 -

}]069.01{}253.01{}038.01{}218.01{}0002.01{}011.01{}217.01[{1)(

−−−−−−−−=

xxxxxxtFIPP

594.0)8760( =IPPF

c) Low Press Temperature gate

)}](1)}.{(1)}.{([{1)( // tFtFtFtF snsrtpipeslboilerLPT −−−=

336.0}]058.01{}065.01{}247.01[{1)8760( =−−−−= xxFLPT

d) Delaminated Product surface gate

)}](1)}.{(1[{1)( ..min tFtFtF LPTleakpltnsHationDela −−−=

456.0}]336.01{}182.01[{1)8760(min =−−−= xF ationDela

e) Product sticking on surface plate gate

)}](1)}.{(1[{1)( . tFtFtF LPTQualityPulpSticking −−−=

336.0}]336.01{)}(1[{1)8760( . =−−−= xtFF QualityPulpSticking

f) Wet Lap Misalignment gate

}]099.01[{1)}](1)}.{(1)}.{(1[{1)( / −−=−−−−= tFtFtFtF crctPpusherTGntMisalignme

099.0)8760( =ntMisalignmeF

• Hydrauphore system failure gate

))](1)).((1(

)).(1)).((1[(1)(

tan.2#/

1#//.

tFtF

tFtFtF

kHdrphrvvR

vvRCompAsysHdrphr

−−

−−−=


- 134 -

350.0}]025.01{}183.01{

}183.01{}002.01[{1)8760(.

=−−

−−−=

x

xxF sysHdrphr

• Low Pressure Pumps assembly failure gate

))().()( 2#1# tFtFtF lpplppLPPs =

013.0117.0117.0)8760( == xFLPPs

• Valve No. 5 assembly failure gate

)}](1)}.{(1[{1)( 5$/1.5#/ tFtFtF vvSassyVV −−−=

087.0}]041.01{}049.01[{1)8760(.5#/ =−−−= xF assyVV

g) Press Not Pressing Pulp Mats gate

)}](1)}.{(1)}.{(1)}.{(1.{)}.(1)}.{(1)}.{(1[{1)(

..5#/

tan.11#/6#/.

tFtFtFtFtFtFtFtF

sysHdrphrLPPsassyVVNRV

kstvvvvmatsPLP

−−−−−−−−=

}]350.01{}013.01{)087.01{}039.01{}005.01{}048.01{}217.01[{1)8760(.

−−−−−−−−=

xxxxxxF matsPLP

574.0)8760(. =matsPLPF

h) Overall system probability of failure (Top main gate)

)}](1)}.{(1)}.{(1)}.{(1{

)}.(1)}.{(1)}.{(1[{1)(

.min

/

tFtFtFtF

tFtFtFtF

matsPLPentMisallignmStickingationDela

LPTIPPopsSPRESS

−−−−

−−−−=

}574.01{}099.01{}336.01{}456.01{}0336.01{}594.01{343.01[{1)8760(

−−−−−−−−=

xxxxxxFPRESS

97.0)8760( =PRESSF


- 135 -

4.1.4 Reliability Block Diagram Analysis

Figure 38: Pressing system RBD


- 136 -

4.1.4.1 Estimating the system reliability To analyze this complex system, we will break it down to sub-

assemblies, and calculate the reliability of each assembly separately,

and then combined them back together as group assemblies.

• High pressure pumps HPPs

Figure 39: High pressure pumps 1-4 assembly

All the pumps are identical, so are their thrusters and non-return

valves.

1#1#/.11# .. nrvvvThrustehppassyhpp RRRR = 4.29

74.0961.0810.0953.01# == xxR assyhpp

26.074.011# =−=assyhppF

Since the HPPs are identical and independent. Hence the

reliability of them are also identical.

Therefore;


- 137 -

assyhppassyhppassyhppassyhpp RRRR 4#3#2#1# ===

The assembly is an identical and independent parallel

(K-Out of-n).

Therefore the reliability of such system is:

FPPRHPP34

)41( 4+=− 4.30

72.026.074.0474.0 34)41( =+=− xxRHPP

HPPs 1-4 are in parallel with HPP no.5, therefore;

( )∏=

−−=2

1

11i

HPPs RiR 4.31

( ) )]704.01).(72.01[(1112

1

−−−=−−= ∏=i

HPPs RiR

917.0=HPPsR

• Low pressure pumps sub-assembly The 2 low-pressure pumps are in parallel

Figure 40: Low pressure pumps

( )∏=

−−=2

1

11i

LPPs RiR

)]883.01).(883.01[(1 −−−=LPPsR


- 138 -

986.0=LPPsR

And they are in series with solenoid S1, valve no.8 and change

over valve no.5.

Figure 41: Low pressure pumps assembly

329#/8#/5#/1 ...... SSvvvvvvslppsLPPsSYS RRRRRRRR =

951.0951.0789.0789.0959.0951.0986.0 xxxxxxRLPPsSYS =

506.0=LPPsSYSR

• Hydrauphore sub-assembly

Figure 42: Hydrauphore sub assembly

( )∏=

−−=2

111#//tan/tan.. ].11.[.

ivvvireliefevkHydrphoreCompAkStassyHydrphr RRRRRR

)]817.01)(817.01[(1952.0974.0998.0995.0. −−−= xxxxR assyHydrphr


- 139 -

888.0. =assyHydrphrR

• Heating system

Figure 43: Heating system

snsrTleakPpipesLboilersysH RRRRR ///. ...=

542.0942.0818.0935.0753.0. == xxxR sysH

Remaining sub-assembly

Figure 44: Remaining sub assembly

syspipngsnsrptimerPLC

assybvvassyavvnrvvvRassy

RRRR

RRRRR

..

.7..7.6#/

....

)}].1)(1{(1)}].[1)(1{(1[ −−−−−−=

47.7.7 Savvassybvvassyavv xRRRR ==

863.0951.0908.0.7.7 === xRR assybvvassyavv

981.0)]863.01)(863.01[(17# =−−−=VVR

991.0)]961.01)(783.01[(1.6# =−−−=AssyVVR

58.0999.0782.0989.077.0981.0991.0. == xxxxxxR assyR

FPPR AssyMgntBlkng23

.. 3+=


- 140 -

726.0344.0656.03656.0 23.. =+= xxR AssyMgntBlkng

Figure 45: Press assembly

)}]1)(1)(1)(1{(1.[ ./... fanExTGspoPRamsassymgntBlkngpress RRRRRR −−−−−=

71.0)}]904.01)(901.01)(548.01)(48.01{(1[726.0 =−−−−−= xRpress

Now that the complex configuration has been reduced to a simple

assembly, we can estimate the whole system reliability.

Figure 46: Pressing plant simplified RBD

essRassysysHeatingAssyLPPsAssyHPPssystemeHydrauphorplantP xRxRxRxRxRRR Pr..... =

71.058.0542.0506.0917.0888.0. xxxxxR plantP =

16.0. =plantPR

4.1.5 Estimating the system Availability To analyze this complex system, we will break it down to sub-

assemblies, and calculate the Availability of each assembly separately,

and then combined them back together as group assemblies.


- 141 -

• High pressure pumps HPPs All the pumps are identical, so are their thrusters and non-return

valves.

1#1#/.11# .. nrvvvThrustehppassyhpp AAAA =

959.0961.0999.0999.01# == xxA assyhpp

Since the HPPs are identical and independent, hence the

availability of them is also identical.

Therefore;

assyhppassyhppassyhppassyhpp AAAA 4#3#2#1# ===


(K-Out of-n).

Therefore the Availability of such system is:

989.0041.0

959.04959.04 3434)41(

=

+=+=−

x

xUAAAHPP 4.32


( )∏=

−−=2

1

11i

HPPs AiA

( ) )]999.01).(989.01[(1112

1

−−−=−−= ∏=i

HPPs AiA

99.0=HPPsR


( )∏=

−−=2

1

11i

LPPs AiA


- 142 -

)]999.01).(999.01[(1 −−−=LPPsA

999.0=LPPsA


over valve no.5.

329#/8#/5#/1 ...... SSvvvvvvslppsLPPsSYS AAAAAAAA =

951.0951.0999.0999.0999.0951.0999.0 xxxxxxALPPsSYS =

85.0=LPPsSYSA


( )∏=

−−=2

111#//tan/tan.. ].11.[.

ivvvireliefevkHydrphoreCompAkStassyHydrphr AAAAAA

)]999.01)(999.01[(1999.0999.0998.0999.0. −−−= xxxxA assyHydrphr

99.0. =assyHydrphrA

• Heating system

leakPlatensnsrTpipesLboilersysH AAAAA .//. ...=

99.0999.0942.0999.0999.0. == xxxA sysH

• Remaining sub-assembly



AAAA

AAAAA

..

.7..7.6#/

....

)}]1)(1{(1)}].[1)(1{(1[ −−−−−−=

47.7.7 Savvassybvvassyavv xAAAA ==

950.0951.0999.0.7.7 === xAA assybvvassyavv


- 143 -

997.0)]950.01)(950.01[(17# =−−−=VVA

999.0)]961.01)(999.01[(1.6# =−−−=AssyVVA

UAAA AssyMgntBlkng23

.. 3+= 4.33

726.0344.0656.03656.0 23.. =+= xxA AssyMgntBlkng

768.0782.0989.0999.0999.0997.0999.0. == xxxxxxA assyR

)}]1)(1(

)1)(1{(1.[

.

/...

fanExTGs

poPRamsAssyMgntBlkngpress

AA

AAAA

−−

−−−=

726.0

)}]904.01)(999.01)(999.01)(997.01{(1[726.0

=

−−−−−= xApress


assembly, we can estimate the whole system Availability.

essRassysysHeating

AssyLPPsAssyHPPssystemeHydrauphorplantP

xAxAA

xxAxAAA

Pr.

.... =

726.076.0993.0857.0999.0995.0. xxxxxA plantP =

47.0. =plantPA

4.2 Monte Carlo Simulation

Monte Carlo simulation is a method for iteratively evaluating a

deterministic model using sets of random numbers as inputs. This

method is often used when the model is complex, nonlinear, or

involves more than just a couple uncertain parameters. A simulation

can typically involve thousands evaluations of the model, a task which

in the past was only practical using super computers [193]. With this

method, a large and complex system can be sampled in a number of


- 144 -

random configurations, and that data can be used to describe the

system as a whole.

To be able to handle the complexities and strong dependencies

of the system, we will use a computer program which employs Monte

Carlo simulation methods to estimate system and sub-system

parameters, such as unavailability, expected number of failures, costs

etc.

Also simulation can handle the reliability behavior of repairable

components with non-constant failure rates.

The process involves synthesizing system performance over a

given number of simulation runs [194]. Each simulation run emulates

how the system might perform in real life based on the input data

provided to the fault tree or the reliability block diagram RBD, which in

turn will inform the program how the components failures interact to

cause the system failures, while the failure and maintenance

parameters will inform the program on how often the components are

likely to fail and how quickly they can be restored to service.

Performing many simulation runs enables the program to build a

statistical picture of the system performance by emulating the chance

variations. This is done by using the Microsoft run time library to

generate pseudo random numbers.

Figure 47 shows the simulation sequence that we will use.

In addition to the Failure Mode, Effects and Criticality Analysis

FMECA to further assess the reliability of the system, we will use Fault

Tree Analysis FTA and Reliability Block Diagram RBD, but before we

construct the Fault Tree Analysis FTA and the reliability Block Diagram


- 145 -

RBD models and simulate them, we need to estimate the parameters

of the system components, i.e. MTBF, MTTR. This will be one by

means of fitting the history failure data that we gathered from the plant

performance and lost time database into distribution models and

estimate the required parameters.

Figure 47: Illustrates the used simulation sequence


- 146 -

4.3 Evaluating the system performance To evaluate the system performance and predict its future

behavior based on its history, we accelerate its time performance by

simulating the existing system with the current maintenance policies to

obtain its data. This is done by keying in the program the current

maintenance policy for each component, its frequency, used spare part

and its cost, number of technicians and their hourly rates

The process will involve synthesizing the system performance

over a given number of simulation runs using a computer program.

Each simulation run will in effect emulates how the system might

perform in real life based on the input data that we provide. The input

data can be divided into two categories- a failure logic diagram and

quantitative failure and maintenance parameters. The logic diagram

(FT & RBD) informs the computer program how component failures

interact to cause system failures. The failure and maintenance

parameters inform the program how often components are likely to fail

and how quickly they will be restored to service. By performing many

simulation runs the computer program can build up a statistical picture

of the system performance by recording the results of each run.

The Monte Carlo simulation must emulate the chance variations,

which will affect system performance in real life, to do this the

computer must generate random numbers, which form a uniform

distribution, and since the rules used on the computer to generate the

number sequences are deterministic then the numbers will be pseudo-

random producing a uniform distribution [194].

The cumulative failure distribution at time t for a three-parameter

Weibull distribution is;


- 147 -

β

ηγ⎟⎟⎠

⎞⎜⎜⎝

⎛ −−

−=t

etF 1)(

By setting 0=γ and taking the log of both sides of this equation

we obtain

))(1ln( tFt−−=⎟⎟

⎠

⎞⎜⎜⎝

⎛β

η 4.34

Re-arranging the above equation

))(1ln( tFt −−=η 4.35

With this equation, we can sample times to failure TTF for the

component by first generating a set of uniform random numbers x1, x2,

x3, x4, etc. (between 0 and 1 ) and then substituting these values as

F(t) into the equation above

Table 39: Illustrates obtaining TTF values by simulation

By following the same procedure for the repair models of the

system, we obtain the following expression:

))(1ln(. tGMTTRt −−= 4.36

Where, G(t) is the cumulative repair distribution.

Based on the number of simulations, the following estimates can

be made:


- 148 -

Mean Unavailability = total downtime/total simulation time 4.37

Unreliability at (t) = number of times system failed at

least once during a simulation / no. of simulations 4.38

We re-arrange the failure modes, to analyze the system

maintenance policies and their effect.

Appendix 2 shows the system hierarchy

To determine how effective (beneficial) each maintenance task is,

we will calculate the criticality values, which represents the severity of

the effects associated with the cause combined with its frequency of

occurrence from the following equations for each failure mode:

1) Cost Benefit Ratio (CBR).

alarmsandtasksspecifiedwithoutCostalarmsandtasksspecifiedwithCostCBR.....

.....= 4.39

A cost benefit ratio less than 1 indicates that the

task/alarm is worthwhile from a cost point of view.

2) Safety/Environmental Ratio (SBR).

alarmsandtasksspecifiedwithoutycriticalitSafetyalarmsandtasksspecifiedwithycriticalitSafetySBR......

......= 4.40

A safety/environmental benefit ratio less than 1

indicate that the task/alarm is worthwhile from a

safety/environmental point of view.


- 149 -

3) Operational Benefit Ratio (OBR).

alarmsandtasksspecifiedwithoutycriticalitlOperationaalarmsandtasksspecifiedwithycriticalitlOperationaOBR......

......= 4.41

An operational benefit ratio less than 1 indicate that

the task/alarm is worthwhile from a operational point of

view.

To estimate the system parameters, we assign the following:

• The estimated failure and repair parameters.

• The maintenance policies associated with the component

or equipment imported from CMMS.

• The different costs associated with these maintenance

policies, i.e. type of spare parts and its cost, number of

labor performing the maintenance task, the duration of

the task and the labor rate

After assigning all the parameters to the model, we will simulate

the process and estimate the system availability and different cost

associated with it.

Appendix 1 shows the labor data used.

Appendix 2 shows the spare parts data used.

Appendix 3 shows the different maintenance policies of the

equipment as extracted from the plant CMMS.

Appendix 4 shows the system effects and their respective CBR,

SBR and OBR before optimization.


- 150 -

Calculating the system effects data by simulation, set at 8760 hrs,

we obtain the following results

From the simulation analysis we obtain the system unavailability,

unreliability and mean failure frequency.

Where, Unavailability (U) =1- Availability (A)

MTTRMTBFMTBFA +=

R(t)=1-F(t), where t, is the system lifetime and is equal to 8760

hrs. t

etFλ−

−= 1)( For exponentially distributed components, and

tetR λ−=)( , where λ is the failure rate=1/MTTF

For Weibull distribution; β

η ⎟⎟⎠

⎞⎜⎜⎝

⎛−

−=t

etF 1)(

Mean Failure Frequency between or Average Failure Frequency

between (0,T) is

TTRTAFR )(ln),0( −

= 4.42

For repairable system;

N(t) is the counting function that keeps track of the cumulative

number of failures a given system has had from time zero to time t,

and its a step function that jumps up one every time a failure occurs

and stays at the new level until the next failure.

M(t) = the expected number (average number) of cumulative

failures by time t for these systems.


- 151 -

m(t) is The derivative of M(t),, is defined to be the Repair Rate or

the Rate Of Occurrence Of Failures at Time t or ROCOF.

For Homogeneous Poisson Process (HPP)

M(t) =λt 4.43

m(t)=λ, the repair rate ROCOF t

etFλ−

−=1)(

For Non -Homogeneous Poisson Process (HPP)

])[(1)(ββη TtT

T etF −+−−= 4.44

T is the time of the just occurred failure

β is the shape parameter, and

η is the characteristic life.

Table 40, shows the system mean failure frequency, unreliability

and mean unavailability (system profile) through the system lifetime,

before optimization, which is set at 8760 hrs.


- 152 -

Table 40: System profile before optimizing

Table 41 shows the system effects data before optimization

through the system lifetime, which is set at 8760 hrs.

Figure 48 shows the unavailability graph of the plant sub-systems


- 153 -

Figure 48: Sub-systems unavailability profiles before optimization

Table 41: System effects data before optimization

Appendix 5 shows the mean unavailability over lifetime, number

of expected failures, total downtime, unavailability and unreliability at

lifetime (8760hrs) of the system components before optimization.


- 154 -

Figure 49: System cost profile before optimization

From the simulation we obtain the following results:

Labor costs: $96833.1

Spare usage costs: $7383.0

Miscellaneous costs: $2503.21

Effects costs: $3157270 Total costs: $3263989.31

4.4 Optimizing the system

To optimize the system, we have to first assign different

maintenance policies to the existing ones. After assign these policies,

we will evaluate them by using CBR, SBR, and OBR. The policies that

meet the previously mentioned criteria, which are a ratio of less than 1,

will be used, and then we will re-evaluate the system and estimate the

different cost associated with the system.


- 155 -

As the analysis is going to be performed by using computer

program, it’s useful to state the different models and mathematical

equation that will be used in the optimization simulation process.

4.4.1 Mathematical models and equations 4.4.1.1 Modeling the optimal replacement times for equipment which it’s

operating cost increases with usage

Total cost in interval=cost of operating + cost of replacement

and=

∫ +rt

rcdttc0

)( 4.45

4.46 Where,

c(t) is the operating cost per unit time at time t after replacement.

Cr is the total cost of a replacement.

C(tr) is the total cost per unit time for the interval(0,tr).

Equation 2, is a model of the problem relating replacement

interval tr to total cost per unit time C(tr).

The optimal replacement interval tr is that the value of tr that

minimizes the right-hand side of equation 2, which can be shown by

calculus to occur when

c(tr)=C(tr)


- 156 -

Thus, the optimal replacement time is when current operating

cost rate is equal to the average total cost per unit time. i.e. the optimal

time to replace is when the marginal cost equals the average cost.

If the trend in operating cost is linear, c(t)=a+bt, then the optimal

replacement interval t* is

t*=√2Cr/b 4.47

If replacement time to be taken into account, such as production

losses incurred due to the duration of the replacement and needs to be

incorporated into the cost of replacement action, then the equation

becomes

4.48

Where;

Tr= is the replacement time, and

tr= is the replacement interval

Figure 50 shows the short-term deterministic optimization

Figure 50: Short term deterministic optimization


- 157 -

Example:

If the trend in the operation cost of an item is of the form

]exp[)( KtBAtc −−= 4.49

Where A=$100,B=$80.and k=0.21/week

A-B≥0, is the operating cost per unit time if no deterioration

occurs.

K, is a constant describing the rate of deterioration

Cr, is the total cost of replacement, and is $100

∫ +−−==rt

rr dtt

ttC

0

]100])21.0exp[80100([1)( 4.50

Table 42: C(tr) values for different values of tr

Evaluating the above model for different values of tr, as shown in

the above table indicates that the optimal value of tr, is at 5 weeks.

4.4.1.2 Modeling optimal preventive replacement interval of an item subject to breakdown

This model relates the replacement interval tp to total cost C(tp).

4.51 Where,

C(tp) is total expected cost per unit time.

Total expected cost in interval (0,tp)=cost of preventive

replacement+ expected cost of failure replacement=


- 158 -

Cp+CfH(tp) 4.52

H(tp) is the expected number of failures in interval (0,tp)

tp is the length of interval

Cp is the total cost of a preventive replacement.

Cf is the total cost of a failure replacement.

Figure 51: Optimal preventive replacement

Example: If given;

Cp=$5, Cf=$10, and failure occurs according to normal distribution

with mean=5weeks, standard deviation=1 week

p

pp t

tHtC

)(105)(

+= 4.53

Taking different values of tp, to calculate different values of C(tp), as seen in the table below


- 159 -

Table 43: C(tp) values for different values of tp

From the table above, we see that the optimal replacement policy

is to perform the preventive replacement every 4 weeks.

A sample calculation of tp=2weeks

)4()3()][0(1[)]5()4()][1(1[)2( −Φ−−Φ++−Φ−−Φ+= HHH 4.54

From the standardized normal distribution table, we obtain;

0)5(,0)4( ≈−Φ≈−Φ

00135.0000135.0)4()3( =−=−Φ−−Φ

0)0( =H

00)0(1[)1( =+= XHH

00135.000135.0)01(0)01()2( =+++= xxH

Therefore,

weekperxC .51.2$2/)00135.0105()2( =+=

4.4.1.3 Modeling of the optimal spare parts preventive replacement age and constant failure interval

4.55

Where,

EN(T,tp) is the expected number of spares required over the planning

period, when preventive replacement occurs at time tp

=Number of preventive replacements in interval (0, T)

+ Number of failure replacements in interval (0, T)


- 160 -

tp is the optimal preventive replacement time(age or period).

T is the planning period

F(t) is the probability density function

[1-R(tp)] is the probability of the failure

M(tp) is the number of expected replacements in tp

Example: If T= 52weeks (1year),tp is 4 weeks, F(t) is 0.16,M(tp)=3.17

EN(52,4)=52/4(4x0.84+3.17x0.16)=13.44 per year

4.4.1.4 Modeling the optimal inspection frequency

Optimal inspection frequency to maximize profit

⎟⎠⎞

⎜⎝⎛

++

−=RVIV

in µλ )(` 4.56

Where,

‘λ(n) is d/dn λ(n)=k/n, k is constant and is the arrival rate of

breakdown per unit time, when 1 inspection is made per unit time

⎟⎠⎞

⎜⎝⎛

++

=IVRVikn

µ 4.57

n is the optimal inspection frequency.

1/µ is the mean time to repair=k

1/i is the mean time to perform inspection

I is the average cost of inspection per uninterrupted unit of time

R is the average cost of repairs per uninterrupted unit of time

V is the profit value if there are no downtime loses.

1/λ is MTTF


- 161 -

)(nλλ ≡

Example: K=0.033 month

1/i=0.011 month

V=$30000

R=$250

I=$125

006.31253000025030000

011.0033.03

=⎟⎠⎞

⎜⎝⎛

++

=xn

And for optimal inspection frequency to minimize downtime

innnD +=

µλ )()( 4.58

Where, D(n) is assumed to be a continuous function of n

µkin = 4.59

Figure 52: Optimal inspection frequency to maximize profit


- 162 -

Figure 53: Optimal inspection frequency to minimize downtime

4.4.1.5 Availability

MTTRMTBFMTBFA +=

4.5 Obtaining the optimized system results

We will select the new maintenance policies by simulating the

existing policies and optimize them using the models in (4.4.1.1-5),

after optimizing these policies we will compare their relevant CBR,

SBR, and OBR ratios, and we select the ones that meets the criteria of

less than 1 ratio.

Appendix 8 shows the new selected maintenance policies, and

appendix 9 shows the new selected maintenance policies, and their

effect on the system.

Re-evaluating the system with the new policies, we obtain the

followings:


- 163 -

Table 44 shows the system effects data after optimization

through the system lifetime, which is set at 8760 hrs.

Table 44: Press system affects data after optimization

Table 45 shows the system profile after optimization through the

system lifetime, which is set at 8760 hrs.


- 164 -

Table 45: System profile after optimization

Appendix 9 shows the mean unavailability over lifetime, number

of expected failures, total downtime, unavailability and unreliability at

lifetime (8760hrs) of the system components after optimization.


- 165 -

Figure 54: Unavailability profile of plant sub-system after optimization

From evaluating the system after applying the new maintenance

policies, we get the following results:

Labor costs: $217,697.13

Spare usage costs: $6,180.00

Miscellaneous costs: $90.0

Outage costs: $2,258,064.00

Total costs: $2,482,031.13

Figure 55 shows the cost profile of the system after optimization


- 166 -

Figure 55: System cost profile after optimization


- 167 -

CHAPTER FIVE - SENSITIVITY ANALYSIS 5. 5.1 Introduction

The cost based optimization process is based on evaluating the

existing maintenance strategies and calculating the criticality values,

which represents the severity of the effects associated with the cause

combined with its frequency of occurrence, the next step will then be to

examine these values, any value equals one or above is considered

not beneficial from the point of view of; cost, safety/environmental and

operations, after which is to select new maintenance strategies for the

items that have values more than one, and examine their effect on

these critical values.

5.2 Comparing system data with and without dependencies before optimization

We will estimate the system reliability and availability after

including the dependencies and compare the results with the ones

obtained from theoretical assessment using the same equations used

for assessing the system reliability and availability theoretically.

5.2.1 Estimating the simulated system reliability

1#1#/.11# .. nrvvvThrustehppassyhpp RRRR =

28.092.042.074.01# == xxR assyhpp

72.028.011# =−=assyhppF

Since the HPPs are identical and independent. Hence the

reliability of them are also identical.

Therefore;

assyhppassyhppassyhppassyhpp RRRR 4#3#2#1# ===


- 168 -


(K-Out of-n).

Therefore the reliability of such system is:

FPPRHPP34

)41( 4+=−

70.072.028.0428.0 34)41( =+=− xxRHPP


( )∏=

−−=2

1

11i

HPPs RiR

( ) )]72.01).(70.01[(1112

1

−−−=−−= ∏=i

HPPs RiR

916.0=HPPsR


( )∏=

−−=2

1

11i

LPPs RiR

)]69.01).(69.01[(1 −−−=LPPsR

904.0=LPPsR


over valve no.5.

329#/8#/5#/1 ...... SSvvvvvvslppsLPPsSYS RRRRRRRR =

91.091.078.078.095.091.0904.0 xxxxxxRLPPsSYS =

414.0=LPPsSYSR


- 169 -


( )∏=

−−=2

111#//tan/tan.. ].11.[.

ivvvireliefevkHydrphoreCompAkStassyHydrphr RRRRRR

)]81.01)(81.01[(1952.097.089.097.0. −−−= xxxxR assyHydrphr

768.0. =assyHydrphrR

• Heating system

snsrTleakPpipesLboilersysH RRRRR ///. ...=

391.094.085.071.069.0. == xxxR sysH

Remaining sub-assembly



RRRR

RRRRR

..

.7..7.6#/

....

)}].1)(1{(1)}].[1)(1{(1[ −−−−−−=

47.7.7 Savvassybvvassyavv xRRRR ==

828.091.091.0.7.7 === xRR assybvvassyavv

970.0)]828.01)(828.01[(17# =−−−=VVR

994.0)]98.01)(72.01[(1.6# =−−−=AssyVVR

656.099.078.099.089.0970.0994.0. == xxxxxxR assyR

FPPR AssyMgntBlkng23

.. 3+=

633.031.069.0369.0 23.. =+= xxR AssyMgntBlkng

)}]1)(1(

)1)(1{(1.[

.

/...

fanExTGs

poPRamsassymgntBlkngpress

RR

RRRR

−−

−−−=


- 170 -

62.0)}]90.01)(42.01)(54.01)(46.01{(1[633.0 =−−−−−= xRpress


assembly, we can estimate the whole system reliability.

essRassysysHeatingAssyLPPsAssyHPPssystemeHydrauphorplantP xRxRxRxRxRRR Pr..... =

62.0656.0391.0414.0916.0768.0. xxxxxR plantP =

046.0. =plantPR

5.2.2 Estimating the simulated system Availability

• High pressure pumps HPPs All the pumps are identical, so are their thrusters and non-return

valves.

1#1#/.11# .. nrvvvThrustehppassyhpp AAAA =

64.096.076.088.01# == xxA assyhpp

Since the HPPs are identical and independent, hence the

availability of them is also identical.

Therefore;

assyhppassyhppassyhppassyhpp AAAA 4#3#2#1# ===


(K-Out of-n).

Therefore the Availability of such system is:

93.012.088.0488.04 3434)41( =+=+=− xxUAAAHPP



- 171 -

( )∏=

−−=2

1

11i

HPPs AiA

( ) )]82.01).(93.01[(1112

1

−−−=−−= ∏=i

HPPs AiA

99.0=HPPsA


( )∏=

−−=2

1

11i

LPPs AiA

)]78.01).(78.01[(1 −−−=LPPsA

95.0=LPPsA


over valve no.5.

329#/8#/5#/1 ...... SSvvvvvvslppsLPPsSYS AAAAAAAA =

92.092.091.091.096.092.095.0 xxxxxxALPPsSYS =

59.0=LPPsSYSA


( )∏=

−−=2

111#//tan/tan.. ].11.[.

ivvvireliefevkHydrphoreCompAkStassyHydrphr AAAAAA

)]99.01)(99.01[(198.099.093.097.0. −−−= xxxxA assyHydrphr

87.0. =assyHydrphrA


- 172 -

• Heating system

leakPlatensnsrTpipesLboilersysH AAAAA .//. ...=

48.09.094.066.086.0. == xxxA sysH

• Remaining sub-assembly



AAAAAAAAA

..

.7..7.6#/

....)}]1)(1{(1)}].[1)(1{(1[ −−−−−−=

47.7.7 Savvassybvvassyavv xAAAA ==

86.092.094.0.7.7 === xAA assybvvassyavv

98.0)]86.01)(86.01[(17# =−−−=VVA

999.0)]961.01)(999.01[(1.6# =−−−=AssyVVA

UAAA AssyMgntBlkng23

.. 3+=

84.025.075.0375.0 23.. =+= xxA AssyMgntBlkng

67.099.079.099.091.098.098.0. == xxxxxxA assyR

)}]1)(1)(1)(1{(1.[ ./... fanExTGspoPRamsAssyMgntBlkngpress AAAAAA −−−−−=

75.0)}]90.01)(86.01)(77.01)(89.01{(1[75.0 =−−−−−= xApress

Estimating the system Availability

essRassysysHeatingAssyLPPsAssyHPPssystemeHydrauphorplantP xAxAxAxAxAAA Pr..... =

75.067.048.059.099.087.0. xxxxxA plantP =

12.0. =plantPA


- 173 -

Using the same calculation, but substituting the values obtained

from the optimization simulation, we obtain the following results;

1. System reliability: 0.34

2. System availability: 0.30

Table 46: comparison of system reliability & availability with and without dependencies,

before and after optimization

By comparing the system reliability and availability with

dependencies and without them as shown in table 46, we can see that

there is a substantial difference between the reliability and availability

of both assessments. This highlights the effect of the dependencies on

the system.

Also by comparing the results before and after optimization and

with dependencies, we can see clearly the effect of the new proposed

condition based maintenance strategies in enhancing the system

overall reliability and availability.

We have to mention that the results of the optimization does not

take into consideration the low pressure pumps system, as they will be

redesigned, therefore we cannot estimate their values. So far there are

no models that can incorporate the dependencies such as; the cost of


- 174 -

various equipment that are used to carry out the maintenance task, the

hourly rate of the labor, the number of labor used for each task, and

the number of spare parts used. Its associated costs and the time

taken to mobilize and carry out the maintenance task, all together into

a single model, hence, simulation is closest method to carry out the

assessment.

The low-pressure pumps were not included in the calculations of

reliability and availability after optimization, because the simulation

results suggested the redesign of these pumps. This is in line with the

fact that these pumps flagged for redesign for few years now by the

management.

Appendix 11 illustrates the reliability and availability of the

different system components before and after optimization.

5.3 Comparing the number of failures and downtime

The effect of the number of failures and subsequently the total

downtime of the different components of the system is proportional to

the cost of maintaining the system. Table 47 illustrates the effect of

optimization on reducing the number of expected number of failures

and the subsequent reduction in expected downtime; it clearly shows

the effect of the new maintenance strategies on the system behavior

and the substantial reduction in the number of expected failures and

system downtime.


- 175 -

Table 47: the number of expected failures and downtime of the System before and after optimization

5.4 Criticality values estimation

To determine how effective (beneficial) each maintenance task is

we calculate the criticality values, which represents the severity of the

effects associated with the cause combined with its frequency of

occurrence. The following equations illustrate how these values are

calculated for each failure mode:

4) Cost Benefit Ratio (CBR).

alarmsandtasksspecifiedwithoutCostalarmsandtasksspecifiedwithCostCBR.....

.....=

A cost benefit ratio less than 1 indicates that the

task/alarm is worthwhile from a cost point of view.

5) Safety/Environmental Ratio(SBR).

alarmsandtasksspecifiedwithoutycriticalitSafetyalarmsandtasksspecifiedwithycriticalitSafetySBR......

......=

A safety/environmental benefit ratio less than 1

indicate that the task/alarm is worthwhile from a

safety/environmental point of view.


- 176 -

6) Operational Benefit Ratio. (OBR).

alarmsandtasksspecifiedwithoutycriticalitlOperationaalarmsandtasksspecifiedwithycriticalitlOperationaOBR......

......=

An operational benefit ratio less than 1 indicate that

the task/alarm is worthwhile from a operational point of

view.

Appendices 6 and 9 show these values before and after

optimizing the system (applying the new maintenance strategies).

5.5 Comparing the different cost associated with the system before and after optimization

By comparing the two results obtained before and after system

optimization, we calculate the following cost saving from optimization:

1) Labor cost savings:

$96,833.10 - $217,697.13

=-$120,864.00

2) Spare parts usage savings: $7,383.00- $6,180.00

=$1,203.00

3) Miscellaneous cost savings: $2,503.21 - $90.00

= $2,413.21

4) Effects cost savings: $3,157,270 - $2,258,064

= $899,206


- 177 -

Estimated Total cost savings= Labor cost savings+ Spare parts

usage savings+ Effects cost savings

=-120,864 + 1,203 + 2,413.21 + 899,206

= $781,958.18

5.6 Conclusions The system use mix maintenance strategies, with the emphasis

being on scheduled maintenance strategies, this approach has proved

to be un useful in terms of analysis and in real life application. The new

approach maintains the mix strategies policy, but puts the emphasis on

the inspection. From the above analysis, it’s clear that although there

is an increase in the labor costs, and increase in the frequency of the

inspection times, but in return;

• It has reduced the frequency of the scheduled or preventive

maintenance strategies, extending the times between these

tasks.

• The savings in the effects cost is high, which in turn yields a

positive savings of about %16 from the annual maintenance

budget.

With this reduction in the maintenance cost, it brings the budget

in line with the business objectives.


- 178 -

CHAPTER SIX – CONCLUSIONS

6.1 Introduction In this study, a cost based reliability program for optimizing the

performance of a complex fiberboard pressing plant introduced, using

the Monte- Carlo method, which utilizes stochastic simulation models

as the platform for evaluating the existing system based on its current

maintenance strategies and their effects on the system at different

levels of this system. This platform has enabled the creation of plant

model that incorporates the system parts at different levels of the

hierarchy, with its dependencies, and analyzes their behavior in the

future. By knowing those behaviors we can use both engineering

judgment and the software features to introduce new maintenance

strategies such as, run to failure strategy, preventive strategies,

condition based, inspection, redesign etc, which will help reduce the

ever increasing maintenance cost and plant Unavailability.

The plant's operating concept and most of its equipment are over

50 years old; hence, its maintenance strategies have not changed

much since, although few systems have been introduced to modernize

it. With the Challenges facing this industry, this program along with the

new CMMS introduced by the researcher as a first step towards

establishing a sound maintenance program in the plant to replace the

outdated and limited functionality system, provides a good tool for the

maintenance personnel to make the right decision concerning their

equipment, as it can be easily carried out on the rest of the plant using

the same method of analysis, thus enabling the management to focus

on other aspects to improve the business rather than concentrating

their efforts on how to reduce the plant downtime and control the

continuously increasing maintenance budget.


- 179 -

Maintenance policies based on mathematical models are much

more flexible than heuristic policies. Mathematical models can

incorporate a wide variety of assumptions and constraints, but in the

process they can become quite complex. A great advantage of the

mathematical approach is that the outcomes can be optimized.

Optimization with regard to changes in some basic model parameter

can be carried out for maximal reliability or minimal costs.

Mathematical models can be deterministic or probabilistic. Since

maintenance models are used for predicting the effects of

maintenance in the future, probabilistic methods are more appropriate

than deterministic ones, even if the price for their use is increased

complexity and a consequent loss in transparency. For these reasons,

the use of such methods is spreading only slowly.[195].

6.2 Major findings

The woodchip and pulp or engineered wood industry in Australia

and around the world is a lucrative industry; the industry's turnover in

Australia was $9.91 billion, or around one per cent of GDP in 1992-93

(latest available data). The industry employs approximately 82,500

people, according to the latest labor force estimates from the

Australian Bureau of Statistics. It’s a mature industry with a strong

market. Here in Australia, the Australian timber industry is going

through unprecedented change. There are significant opportunities for

growth in the production and sales of high value timber products in all

Australian species groups [1].

The predicted volume of hardwood pulpwood produced in

Australian plantations will increase from around 0.7 million cubic

meters per annum in the 1995-99 period to over 10 million cubic


- 180 -

meters per annum in the period 2035-39 [3]. The increase in the

population, the rising demand for the wood products to be used in the

housing industry [4], and since hardboard has established itself as a

reliable product for use in the dwelling construction, furniture and

cabinetry industry for its unique characteristics, it is forecast that it still

can retain its niche market if it can introduce new technologies and

reduce its maintenance cost.

Industry related maintenance research and study:

• The Hardwood (engineered wood) industry uses the wet

process, this type of processes requires an extensive and high

cost maintenance, combined with its ageing equipment have

saw a continuously rising maintenance cost (about 40% of the

operation cost). The candidate study of literature revealed no

studies has been done (or at least has been published) that

addresses the issue of maintenance in this industry.

• Inspection strategies Frequency:

The key factors that this study is based on to determine

how effective (beneficial) each maintenance task is, are; the

cost benefit ratio (CBR), safety benefit ratio (SBR) and

operational benefit ratio (OBR). A benefit ratio of less than one

indicates that the maintenance task/alarm is worth doing. These

values are the criticality values, which represents the severity of

the effects associated with the cause and combined with its

frequency of occurrence for each failure mode. The study

results shows that by increasing the inspection policies

frequency over the calculated system life time (in this instance


- 181 -

8760 hrs), these benefit ratios are decreased noticeably,

although this increases the cost of these inspections but the

overall result is reduction in the total maintenance cost.

• The effects of the use of mix maintenance strategies

on the expected number of failures:

The system under study uses the following mixture of maintenance policies to maintain it;

• Breakdown maintenance

• Preventive maintenance

• Statutory Maintenance

• Condition based maintenance

With the higher emphasis are being on the preventive

maintenance strategies. Although these strategies maintains the

system to a certain extend but they are timely consuming,

financially exhausting and labor intensive practices. To be able

to optimize the system and obtain better results that can

increase the availability and reliability of the system, the

research findings suggest the reduction of the preventive

strategies and increase in the condition based strategies.

Analyzing the obtained results shows that some of the

preventive strategies when replaced with inspection or other

form of condition based maintenance in some cases, and

combined the two strategies but with increase of CBM

frequency in other cases, yields the results of 56% reduction in

expected number of failures, or about 275 failures, and overall

reduction in downtime of about 50%, or about 438 hrs and as

shown in appendix 11.


- 182 -

6.3 Limitations and Uncertainties:

• Although this type of industry has been around since the fifties,

so it’s considered as a mature industry, but so far there is no

literature available, this have made it hard for any researcher to

identify the type of work that has been carried out to develop

this industry.

• The absence of real life data for some of the equipments. This

has forced the candidate to rely on the memory recall of events

by the available staff and their accuracy of these events.

• Some data were found on hard copies as a remarks made by

different maintenance managers and engineers, who managed

the maintenance department throughout the years. This was a

tedious and time consuming process to convert and cleans this

information to a useful data.

• The level of accuracy of the simulation relies heavily on the

availability and credibility of the data.

• Due to the similarity of some of the equipment, an

exchangeable or rotatable spare parts permanent situation was

created, and with absence of maintenance planning and proper

documentation of spare parts rotation & movement and

traceability history, it was difficult to optimize the maintenance

strategies, spares and its replacement.

• Mathematical and statistical models were used to assess the

system and optimize the maintenance cost. Some of these

models are restricted; others are impractical and rely heavily on

assumptions.


- 183 -

• The sensitivity of some of the business performance data and

its confidentiality, have limited the use of it, hence jeopardized

the integrity of the research.

• Some of the equipment failure data was recorded on equipment

level. With the absence of low level (parts or component level)

history data, made it difficult to analyze this equipment at a

lower level and to describe the interaction among these parts or

components.

• The simulation software utilizes fault tree analysis. In FTA

components at low level can affect higher levels only and not

vice versa, hence the components interactive failures at same

level are uncertain and hence cannot be described.

• The lifetime measure of items is uncertain, hence the failure

times are very much randomly variable.

• A degree of uncertainty of the cost arises from the uncertainty of

the maintenance decision taken during coverage time.

• There is some uncertainty about some of the lost time recorded

against the system, but in fact it was outside the system

boundary. Due to the complexity and irregularity in recording the

lost times in the plant database, it was difficult to verify and

exclude these data from the actual system in concern.

• We are also cautious about the simulation results obtained after

optimization for the system reliability and availability, as the

calculations did not include the low pressure pumping system,


- 184 -

due to the fact that it is suggested to redesign the low pressure

pumps, therefore we cannot estimate their parameters for now.

6.4 Future studies

To study the interactive relationship between components

at the same level to understand how this interaction affects on

the behavior of an existing system. Once this is achieved it will

pave the way to the attempt of developing a reliability model

based on the interaction of components at the same level in a

repairable multi components system, this will enhance the

accuracy of FTA by simulation in association with the simulation

software provider. The conventional models of dependant

failures do not cope at all with interactive failures, which are the

failures caused by interaction between different components[38]

The introduction of multi inventory Production Scheduling

(MIPS) method for a system that uses a mixed maintenance

strategies and subject to imperfect repairs.

The issue of production rate, availability of the machines,

set up time & other production issues and it’s relation with

maintenance have become a centre of focus recently. Gur

Mosheiov [196] stated “Scheduling a maintenance activity of the

machines in production systems has become an important

decision for both practitioners and researchers. Often, a

maintenance must be performed within a given time interval.

During the maintenance time, the machine is shut down, and

the production is stopped. Consequently, the completion times

of the jobs processed after the maintenance are delayed.” He

focused on scheduling a maintenance activity on a system of


- 185 -

unrelated parallel machines; his main contribution was the

introduction of a simple heuristic procedure. Liao [197] in his

study integrated maintenance and production programs with the

economic production quantity (EPQ) model for an imperfect

process involving a deteriorating production system with

increasing hazard rate: imperfect repair and rework upon failure

(out of control state), while Chung [198] focused on optimizing

the system reliability in multi factory production network by

maintenance approach, Dehayem [199] suggested a

hierarchical decision making in production and repair/

replacement planning with imperfect repairs under uncertainties,

Fei [200] introduced optimal production run time for a

deteriorating production system under imperfect repairs and

maintenance, and [201] have focused on the optimal

maintenance of a production inventory system with idle periods.

The above studies and others did not deal with the issue of

multi product inventory. Many industrial plants produce multi

products on the same production line, with change in the set up,

the variable production rate for different products and the

decision when to stop production up on discovering of failure,

the severity of the failure. All these uncertainties needs to be

dealt with under one approach, which the candidate believes it

can be a useful future project.


- 186 -

REFERENCES

1. Gooding.Graeme, Market movements and opportunities for hardwood products, in ABARE OUTLOOK 2000 CONFERENCE. 2000: Victoria,Australia.

2. AustralianHardboards. Company Profile. 2006 [cited; Available from: www.australianhardboards.com.au.

3. Love, G. Outlook for Australian Forest Products. in In Proc. of the National Agricultural and Resource Outlook Conference. 1998. Canberra.

4. Stafford, B., Australian Pulp & Paper Industry - Strucutral Change, Strategic Implications, Market Opportunities, in Australia's Paper and Forestry Forum. 1998: Melbourne.

5. Gunnersen, T.H., Why Australia Needs a Hardboard Industry January 1999, NAFI.

6. El Hayek, M., Optimizing life-cycle maintenance cost of complex machinery using advanced statistical techniques and simulation. , in University of New South Wales. School of Mechanical and Manufacturing Engineering 2006, UNSW: Sydney.

7. Tsang, A.H.C., Jardine, A.K.S., Campbell, J.D. and Picknell, J. V., Reliability Centred Maintenance: A key to Maintenance Excellence. 2000, Hong Kong.

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APPENDIXES

APPENDIX 1 COMPONENTS THEORETICAL UNRELIABILITY, RELIABILITY AND

AVAILABILITY (WITHOUT DEPENDENCIES


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APPENDIX 2 SYSTEM HIERARCHY


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APPENDIX 3 LIST OF THE PRESSING PLANT LABOUR


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APPENDIX 4 SPARE PARTS LIST


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APPENDIX 5 CURRENT MAINTENANCE POLICIES AS EXTRACTED FROM THE PLANT CMMS


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APPENDIX 6 EXISTING MAINTENANCE POLICIES’ EFFECT ON THE SYSTEM


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APPENDIX 7 SYSTEM COMPONENTS DATA BEFORE OPTIMIZATION

Description

Number of expected failures before optimization

Total downtime before optimization

Availability at lifetime before optimizatio

Reliability at lifetime before optimization

BOILER 13.60 40.80 0.86 0.69 HPP#1 21.30 31.95 0.88 0.74 HPP#5 14.82 22.23 0.82 0.72 HPP#2 21.30 31.95 0.88 0.74 HPP#3 21.30 31.95 0.88 0.74 HPP#4 21.30 31.95 0.88 0.74 I/L NRV 19.30 14.47 0.98 0.98 L/PIPES 32.76 16.38 0.66 0.71 NRV#1 11.30 5.65 0.96 0.92 NRV#2 11.30 5.65 0.96 0.92 NRV#3 11.30 5.65 0.96 0.92 NRV#4 11.30 5.65 0.96 0.92 P/SENSOR 7.20 5.13 0.78 0.78 PIPING SYS 1.70 0.85 0.99 0.99 PLTN/LEAK 23.65 35.47 0.90 0.85 S10 6.70 6.70 0.92 0.91 S4 6.30 6.70 0.92 0.91 T/GUIDES 14.30 35.75 0.86 0.42 T/SENSOR 4.60 3.20 0.94 0.94

THRUSTER VLV#1 15.80 15.80 0.76 0.42

THRUSTER VLV#2 15.80 15.80 0.76 0.42

THRUSTER VLV#3 15.80 15.80 0.76 0.42

THRUSTER VLV#4 15.80 15.80 0.76 0.42

TIMER 15.30 8.20 0.99 0.99 V/V NO.6 5.60 19.60 0.89 0.72


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VLV7a 5.40 5.21 0.94 0.91 VLV7b 5.40 5.21 0.94 0.91 V/V#11. 5.30 11.54 0.98 0.95 LOW P

PUMPS 11.27 16.90 0.78 0.69 S2 5.70 13,87 0.92 0.91 PLC 8.20 12.30 0.91 0.89 S3 5.70 13,88 0.92 0.91 V/V#9 2.60 7.80 0.91 0.78 V/V#8 2.60 7.80 0.91 0.78 V/V#5 5.30 7.95 0.96 0.95 BLK MGNT# 8.90 8.90 0.75 0.69 BLK MGNT# 8.90 8.90 0.75 0.69 BLK MGNT# 8.90 8.90 0.75 0.69 A/COMP 8.70 10.87 0.93 0.89 RELIEF

V/V#1 0.32 11.71 0.99 0.81 HYPHR TK 2.59 8.34 0.99 0.97 RELIEF

V/V#2 0.25 9.45 0.99 0.81 NRV 19.65 126.33 0.97 0.97 RAMS 1.30 93.60 0.89 0.46 PLATENS

O/P 11.40 92.00 0.77 0.54


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APPENDIX 8 NEW MAINTENANCE POLICIES TO BE EXPORTED TO THE PLANT CMMS


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APPENDIX 9 NEW MAINTENANCE POLICIES’ EFFECTS ON THE SYSTEM AFTER OPTIMIZATION


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APPENDIX 10 SYSTEMS COMPONENTS DATA AFTER OPTIMIZATION


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APPENDIX 11 System components analysis before and after optimization


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APPENDIX 12 The system Failure Mode effects and criticality analysis (FMECA)


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Documents

A cost-based optimization of a fiberboard pressing plant ...eprints.qut.edu.au/30417/1/Monardo_Giani_Thesis.pdf · A cost-based optimization of a fiberboard pressing plant using Monte-Carlo