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8/12/2019 Estrategia Integra Dade Manu Ten Cao
1/12
Development and application of equipment maintenance
and safety integrity management system
Wang Qingfeng a,*, Liu Wenbin a, Zhong Xin b, Yang Jianfeng a, Yuan Qingbin c
a Engineering Research Center of Chemical Technology Safety of the Ministry of Education, Beijing University of Chemical Technology,
P.O. Box 130, 15 Beisanhuan East Road, Beijing 100029, Chinab The First Research Institute of the Ministry of Public Security, Beijing 100048, ChinacJinzhou Petrochemical Company, Jinzhou 121001, China
a r t i c l e i n f o
Article history:
Received 27 June 2010
Received in revised form
4 December 2010
Accepted 16 January 2011
Keywords:
MSI
Maintenance Indicator Decision-making
Maintenance Tasks Optimization
and Formulation
a b s t r a c t
Equipment management in process industry in China essentially belongs to the traditional breakdown
maintenance pattern, and the basic inspection/maintenance decision-making is weak. Equipment
inspection/maintenance tasks are mainly based on the empirical or qualitative method, and it lacks
identication and classication of critical equipment, so that maintenance resources cant be reasonably
allocated. Reliability, availability and safety of equipment are difcult to control and guarantee due to the
existing maintenance deciencies, maintenance surplus, potential danger and possible accidents. In
order to ensure stable production and reduce operation cost, equipment maintenance and safety
integrity management system (MSI) is established in this paper, which integrates ERP, MES, RBI, RCM, SIL
and PMIS together. MSI can provide dynamic risk rank data, predictive maintenance data and RAM
decision-making data, through which the personnel at all levels can grasp the risk state of equipment
timely and accurately and optimize maintenance schedules to support the decision-making. The result of
an engineering case shows that the system can improve reliability, availability, and safety, lower failure
frequency, decrease failure consequences and make full use of maintenance resources, thus achieving the
reasonable and positive result. 2011 Elsevier Ltd. All rights reserved.
1. Introduction
Petrochemical production processes are complicated and highly
continuous,and the process medium is of high temperature,of high
pressure, inammable, explosive and toxic. Equipment in these
plants tends to become lager-scale and highly-automated, and if
certain unpredicted failure occurs, it may lead to huge economic
losses, environmental pollution and catastrophic safety accidents,
not only to themselves but also to the surroundings. Traditional
equipment maintenance and safety management thoughts bear
the characteristics of more inspection, more maintenance andbreakdown maintenance, which are still the dominance in
Chinese petrochemical enterprises. Unpredicted equipment fail-
ures and unplanned maintenance seem to happen more frequently
than ever before, which, substantially inuence the safety cost,
environmental cost and economic cost, making the reliability and
availability of equipment lower than expected. Within many large-
scale plant-based industries, maintenance cost can account as
much as 40% of the total operational budgets (Eti, Ogaji, & Probert,
2006), and therefore it is urgent and indispensable to improve
maintenance effectiveness.
Petrochemical plants around the world are trying to implement
reliability programs to improve plant safety while maintaining
equipment availability (Michel, & Mufeed, 2008).The benetsare so
evident that in several rening and petrochemical factories, main-
tenance budgets have been reduced by up to 50% (Rodney, 2001).
What reliability engineering is, how reliability models can be made
and what kind of data needs to collect have been discussed (Michel,
& Mufeed, 2008). An approach for the integration of RAMS and riskanalysis is developed as a guide in maintenance policies to reduce
the frequency of failures and maintenance costs (Eti et al., 2006).
Well-informeddecisions, basedon the sound reliabilityengineering
principle, have been used in industrial application of RAMmodeling
(Herder, van Luijk, & Bruijnooge, 2008) and the maintenance indi-
cators have been used to evaluate the effects of maintenance
programson performance and safety (Martorell, Sanchez, & Munoz,
1999). RIMAP methodology provides a guideline for making risk-
based decisions for maintenance and inspection planning, which
concentrates inspection activities on the key component which
bring increased safety and availability (Jovanovic, 2004). The* Corresponding author:Tel.: 86 010 64443058/8301, 86 18601128185 (mobile).
E-mail address: wangqf2422@163.com (W. Qingfeng).
Contents lists available atScienceDirect
Journal of Loss Prevention in the Process Industries
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c om / l o c a t e / j l p
0950-4230/$e see front matter 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jlp.2011.01.008
Journal of Loss Prevention in the Process Industries 24 (2011) 321e332
mailto:wangqf2422@163.comhttp://www.sciencedirect.com/science/journal/09504230http://www.elsevier.com/locate/jlphttp://dx.doi.org/10.1016/j.jlp.2011.01.008http://dx.doi.org/10.1016/j.jlp.2011.01.008http://dx.doi.org/10.1016/j.jlp.2011.01.008http://dx.doi.org/10.1016/j.jlp.2011.01.008http://dx.doi.org/10.1016/j.jlp.2011.01.008http://dx.doi.org/10.1016/j.jlp.2011.01.008http://www.elsevier.com/locate/jlphttp://www.sciencedirect.com/science/journal/09504230mailto:wangqf2422@163.com8/12/2019 Estrategia Integra Dade Manu Ten Cao
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feasibility and advantages of integrating the safety management
system (SMS) and the equipment mechanical integrity (MI) by
means of RBI method has been demonstrated, and the essential
requirements forintegratingMI in SMSare discussedto focus on the
mandatory inspections and on critical components (Bragatto,Pittiglio, & Ansaldi, 2009). It can be seen from the above-
mentionedresearch that much literature researchhas been done on
reliability programs, but traditional technologies are onlyapplicable
to some certain stage or aspect of equipment management, the
existing information systems with different functions suchas ERP or
EAM, MES, Equipment Condition Monitoring System, risk assess-
ment tools (i.e. RCM, RBI and SIL), equipment maintenance/perfor-
mance evaluation system and reliability data collection system,
which are related to equipment management have almost been
ignored or isolated each other. Furthermore,there are fewer studies
on the combination of the above-mentioned technologies.
This paper aims to investigate a kind of MSI management
system to improve equipment reliability, availability and safety in
process industry. Based on the technologyof reliability engineering,risk-based management and professional management technology
are used to establish an intelligent maintenance indicator decision-
making model in MSI. This paper rst explains the principles of
equipment integrity management and the intelligent maintenance
indicator decision-making PDCACycling process. RBI, RCM, SIL, ERP,
MES, PMIS are integrated together to provide standard data struc-
ture in support of decision-making analysis and to eliminate
Information Island to share information for various experts in
different departments in process industry. The collection, storage,
loading and exchange of reliability and maintenance data are
complied with the unied standard, and the needed data for
quantitative risk analysis, fault prediction, fault diagnosis, mainte-
nance tasks optimization and formulation can all be achieved.
The next section outlines the framework of the equipmentintegrity management information system in process industry and
discusses the contents and features of equipment integrity manage-
ment. In Section3, we discuss at length some indicators of mainte-
nance decision-making and the intelligent equipment maintenance
indicator decision-making process. In Section4, we emphasize the
importance of MSI training for personnel at all levels. Section 5
illustrates and discusses an application case of MSI system. Conclu-
sions from building andutilizing MSI system is reported in Section 6.
2. Equipment integrity management in process industry
The intrinsic safety of a device relates to its design and quality,
and the safety production in process industry mostly relates to the
quality of installation and the maintenance of devices. For the
already-established equipment, the unreliability might be in
connection with design defects, incorrect manipulations, inadequate
maintenance or inability to predict failures that may occur during
operation (Eti, Ogaji, & Probert, 2007). Although statutory inspection
intervals enforced by legislations on pressure equipment and otherspecial equipments are implemented in China, but the inspection/
maintenance strategy is empirical, qualitative and arbitrary, and the
detection efciency of equipments defects and faults is very low.
Because of lacking the rst hand equipment health examination,
regulation inspection/maintenance tasks usually couldnt prevent
accidents or disasters resulting from equipments. Breakdown and
malfunction of equipment usually lead to biological and chemical
disasters. Statistics show that more than 40% of the disasters which
happened in petrochemical industry were caused by equipment
failure, so it is very important to ensure the integrity of equipment
(Jiang, & Li, 2007).It can beseen from Fig.1 that maintenance models
have experienced several phases, from breakdown maintenance,
preventive maintenance, predictive maintenance, risk-based main-
tenancetowards maintenanceand safety integrity management,andthere actually exists a close relationship between maintenance ef-
ciency and maintenance model. Reliability, availability, maintain-
ability and safety are the key indicators of maintenance efciency,
which are critical in optimizing maintenance model.
2.1. Contents of equipment integrity management
Equipment integrity management consists of two aspects:
management and technology. On one hand, it entails establishing
Abbreviations and Nomenclature
A availability
DCS Distribution Control System
CMMS Computerized Maintenance Management System
EAM Enterprise Asset Management
ERP Enterprise Resource Planning
ETA Event Tree Analysis
FMECA Failure Model Effect and Criticality Analysis
FTA Fault Tree Analysis
M maintainability
MES Manufacturing Execution System
MSI Maintenance and Safety Integrity Management System
MTBF Mean Time Between Failures
MTBO Mean Time Between Outage
MTTF Mean Time To Failure
MTTR Mean Time To Repair
NPP nuclear power plants
PDCA Plan-Do-Check-Adjustment
PM Plant Maintenance
PMIS Predictive Maintenance Information System
R reliability
RBI Risk-Based Inspection
RAMS Reliability, Availability, Maintainability and Safety
RCA Root Cause Analysis
RCM Reliability Centered Maintenance
SIL Safety Integrity Level
SOA Service Oriented Architecture
LCC Life Cycle Cost
U Utilization
ycneiciffEecnanetniaM
Maintenance and Safety
Integrity Management
Historic Development
Breakdown
Maintenance
Preventive
Maintenance
Predictive
Maintenance
Risk-based
Inspection/Maintenance
Fig. 1. Maintenance models and their corresponding maintenance efciency.
W. Qingfeng et al. / Journal of Loss Prevention in the Process Industries 24 (2011) 321e332322
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equipment integrity management system, and on the other hand,
on the basis of risk analysis, it utilizes technical measures in
combination with criterion management to ensure that critical
equipment can operate in stable condition.
There are mainly six elements in equipment integrity manage-
ment: identication and classication of critical equipment,
inspection and preventive maintenance, abnormality management,
quality ensuring, documentation of program and training, which,
not only involves the duty of management but also is concerned
with every staff in relation to the manufacturing process.
Equipment integrity management in process industry often
bears such features as below:
(1) Preventive maintenance is better than breakdown mainte-
nance, so that preventive maintenance plans and measures are
demanded for critical equipment;
(2) Risk rank of equipment is considered, and inspection/mainte-
nance resources should be transferred towards equipment of
higher risk rank;
(3) Process of the integrity management is dynamic and
continuous;
(4) Integrity management means that all equipments of one unit
or one system should be integrity characteristics and therequirements for integrity correlate with the risk rank of each
equipment;
(5) Integrity management runs through the entire process,
including design, manufacture, installation, utilization, main-
tenance and discard;
(6) Integrity management focuses on managing equipment
abnormality, which is often the precursor of failure;
(7) Integrity management requires that standard maintenance
procedures be made strictly and checked periodically for the
purpose of ensuring the working quality.
AsFig. 2 shows, equipment integrity management system can
be divided into four aspects: work execution and review, proactive
maintenance, risk-based management as well as MSI. Risk-basedmanagement which utilizes RBI, RCM and SIL evaluation tools to
identify and classify key equipments is the core content and the
technical support for the system. Risk-based evaluation can be used
to determine the risk rank of equipment, formulate optimal
maintenance tasks, allocate maintenance resources reasonably and
avoid maintenance deciencies/surplus, thus ensuring the reli-
ability of equipment. Preventive maintenance, predictive mainte-
nance and RCA are all proactive maintenance modes which are
applied by the integrity management. Predictive maintenance
information, risk rank of equipment and RAM indicators are the
basis to make inspection/maintenance strategies. In every stage of
the life cycle of equipment, purposeful preventive maintenance and
failure eradication plans are needed, especially for high-risk
equipment. During the work execution and review process, optimal
maintenance tasks are executed through EAM, CMMS or ERP
system, while failure data and maintenance data are recorded
according to certain standards. In the meantime, working tasks are
conrmed and optimized through professional management
programs in terms of lubrication management, operation
management, abnormality management (defect & fault manage-
ment) and archives management, thus ensuring the quality of the
workow.
Abnormality management, which can be divided into preven-
tive management and predictive management, is also important in
the integrity management system (Jiang, & Li, 2007). Some contents
of preventive management coincide with those of intrinsic safety
design. In order to avoid failures which are usually unobvious,
safety protection devices are set up during the reliability designprocess, which needs planned inspection, planned testing and
planned checking to formulate failure-pinpointing tasks. To achieve
the goal of intrinsic safety, the most important thing is to prevent
incipient failure in advance, and through self-diagnosis or self-
recovery, equipment can re-operate in an orderly and stable state
(Gao, & Yang, 2006). Effective preventive management can gener-
ally ensure the safety of individual equipment, but cant ensure the
integral safety of one unit or one system, while predictive
management can fulll this task. It utilizes predictive maintenance
technology in combination of vibration, temperature, pressure,
ow, liquid level, current, corrosion rate and other features to
perform incipient failure diagnosis by vibration analysis, thermo-
graph analysis, ultrasonic analysis and lubrication oil analysis. By
doing so, uncertainty of maintenance, failure frequency and failureconsequence can be reduced, thus minimizing maintenance cost
while improving operational safety (Ray, FIEAust, & CPEng., 2004).
2.2. Equipment integrity management information
systemof process industry
In order to improve utilization efciency of resources and
promote comprehensive management, many Chinese petrochem-
ical enterprises have made efforts to popularize ERP systems, by
which the logistics ow, capital ow and information ow are
integrated so that enterprise resources are effectively exploited.
However, the PM module of ERP system only utilizes twofunctions:
master data and maintenance worksheet of equipment, while the
preventive maintenance module and reliability managementmodule are not sufciently investigated or applied, and for this
reason the ERP system cant satisfy the need for integrity
management. Up to now, some enterprises have set up PMIS based
on condition monitoring technologies, MES based on production
management, EAM system based on the asset management and
CMMS based on maintenance management to promote the
equipment management level. However, the standards of reliability
data of different equipment are not consistent, so that information
is difcult to exchange, thus forming the information islands.
AsFig. 3shows, equipment integrity management refers to an
integral management system, in which there exist information
exchange and workow among every element and every hierarchy.
By using computer technology, web service technology and database
technology, we can establish the equipment integrity management
MSI
LCC SIL
RCM RBI RAM
Predictive
Maintenance
Work
Identification
& Prioritization
RCAPreventive
Maintenance
Lubrication
Management
Operation
Management
Defect/Fault
Management
Archives
Management
ERP-PMEAM
/CMMS
Maintenance & Safety
Integrity Management
Risk Based Management
Proactive Maintenance
Work Execution
& Review
Fig. 2. Equipment integrity management pyramid structure in process industry.
W. Qingfeng et al. / Journal of Loss Prevention in the Process Industries 24 (2011) 321e332 323
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system incorporating RCM, RBI,SIL and other risk evaluation tools on
the basis of SOA. Meanwhile, both online and ofine conditionmonitoring data from PMIS as well as process data from MES have
beenintegratedinto the system platform. Using OPC(OLE for Process
Control) and RFC (Remote Function Call) interface protocols, the data
communication between MSI and MES, ERP has realized separately
based on SOA technology. Equipment integrity management system
can minimize the routine inspection/maintenance work while not
causing negative impacts to equipment performance, product
quality, safety or environment. Process data, condition monitoring
data, historical inspection/maintenance data, key performance
indicator (e.g. RAM, LCC, MTBF, MTTR, failure frequency and failure
consequence) and dynamic risk evaluation data are all integrated to
perform comprehensive analysis through the unied data structure
and manemachine interface. Through the system platform,
personnel at all levels can master equipment operating status timelyand utilize dynamic data to make decisions for the purpose of
ensuring the operational safety and reliability (Deshpande & Modak,
2002).
Equipment integrity management in process industry is
a rigorous, scientic and normalized management model, and this
system is established with risk management as the core and
professional management as the mainline. Lubrication manage-
ment, operation management, abnormality management, archives
management, work identication and optimization management
can be gradually optimized via the system. Professional manage-
ment workow is designed based on the requirements to ensure
work quality, and the workow can be executed through the
system, so that the equipment management model can gradually
transform from function-oriented towards process-oriented. Based
on dynamic data analysis, an indicator decision-making mecha-
nism, which incorporates RAM, dynamic risk ranking and PMIS, hasbeen established to help assess the effectiveness of maintenance
measures. By monitoring the changes of indicators, maintenance
measures can be adjusted. It can be seen from Fig. 3 that the
equipment integrity management system in process industry is
a dynamic PDCA Deming cycle. The equipment fault diagnosis or
fault prediction information from PMIS can predict failure occurs
and failure trends, when the realtime signature strength exceeds
the threshold of alert level or alarm level of failure symptom
signature, fault can be diagnosed and the equipment residual life
can be calculated through fault degradation trend (Fig. 5). RAM is
a statistical and quantitative analysis indicator which reects
equipment reliability, availability and maintainability. Equipment
reliability or availability which falls short of expectation will be
identied and the weakness or failure will be eliminated. Risk valueresults from the combination of the consequence of failure and the
likelihood of failure, the higher risk rank, the more maintenance
resources (i.e. budget, personnel) should be exploited. With the
help of the indicator decision-making mechanism, basic manage-
ment of equipment can be improved, safety production can be
ensured, failure rate and failure consequences can be reduced,
equipment efciency and availability can be raised and mainte-
nance resource allocation can be more effectively and reasonably.
Predictive Maintenance Information System, which is based on
condition monitoring, web services, XML (Extensive Makeup
Language) signature, XML encryption, UML (Unied Modeling
Language) and other technology, is used to incorporate Condition
Monitoring System into equipment integrity management system
in order that it can realize performance monitoring, data collection,
RBI
Assessment Tool
RBI
Assessment Tool RCM
Assessment Tool
RCM
Assessment Tool SIL
Assessment Tool
SIL
Assessment Tool
RBI
Interface Module
RBI
Interface Module
MTBFMTBF
RAMRAM
RCARCA
Expert Revision
Module
Expert Revision
Module
Preventive
Maintenanc
PreventiveMaintenanc
Predictive
Maintenance
Predictive
Maintenance
Defect/Fault
Management
Defect/Fault
Management
Condition Based
Maintenance
Condition BasedMaintenance
Operation
Management
Operation
ManagementWork Identification
& Prioritization
Work Identification
& Prioritization Lubrication
Management
Lubrication
Management
RCM
Interface Module
RCM
Interface Module
SIL
Interface Module
SIL
Interface Module
Reliability Standard
Reference Data
Equipment Archival
Data
Inspection
/Maintenance
Work Program
Inspection
/Maintenance
History Database
PMPM
MMMM
COCO
PSPS
Master DataMaster Data
-SAP-ERP
SOA
LCCLCC
Plan
Do
Check Adjustment
RCARCA
RCARCA
MTTRMTTR
One-time changes
Maintenance
One-time changesMaintenance
Archieves
Management
Archieves
Management
Failure
Frequency
Failure
Frequency
Failure
Consequences
Failure
Consequences
Dynamic
Risk
Dynamic
Risk
MES
On-line
Condition
monitoringOff-line
Condition
monitoring
PMIS
Performance
monitoring
Fig. 3. MSI framework and roadmap of the intelligent equipment maintenance indicator decision-making in process industry.
W. Qingfeng et al. / Journal of Loss Prevention in the Process Industries 24 (2011) 321e332324
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automatic failure diagnostics and failure prediction (Gao, 2001),
which can help determine inspection/maintenance measures, and
by doing so, failure diagnosis and maintenance decision-making
have been integrated. Condition-based maintenance techniques
utilized by PMIS include vibration analysis, infrared thermograph
analysis, lubrication analysis, tribology analysis, ultrasonic analysis,
motor current analysis, performance analysis, corrosion analysis
and so on. They aim to reduce unexpected failures via condition
monitoring and conduct remedial actions in case failure happens.
For rotating machines, rotor imbalance, shaft misalignment, shaft
cracking, blades rubbing, blade abnormality, blades stall/surge,
uidlm bearing wear, oil whirl, oil whip and other failures can be
automatically diagnosed or predicted through condition moni-
toring analysis. Different failures have different characteristic
signals, and it is assumed that the strength of the signals may
represent the severity of failures. Based on this assumption, the
optimal inspection/maintenance contents and periods can be
determined. Both automatic and manual failure diagnosis messages
underlie the maintenance decision-makings.
3. Intelligent equipment maintenance indicator
decision-making model
Martorell, Villanueva, and Carlos (2005)studied the application
of RAMS genetic algorithms-based information decision-making in
multi-objective optimization of maintenance and technical speci-
cations.Blanchard, Verna, and Peterson (1995)described how to
employ FMECA, FTA and ETA to perform RAMS and LCC risk anal-
ysis.Herder et al. (2008)investigated the industrial application of
RAM.Paul and Funkhouser (2007) studied equipment integrity and
risk analysis for reneries and chemical plants.Warburton, Strutt,
and Allsop (1998) presented a methodology for predicting char-
acteristics of mechanical-failures.Martorell et al. (1999)researched
the utilization of maintenance indicators that can evaluate the
performance of NPP and the effectiveness of maintenance
programs. No matter which method is selected for maintenance
decision-making, reliability prediction, risk evaluation and histor-ical failure data is necessary, while the lack of adequate reliability
data and maintenance data may lead to the difculty in failure
prediction and failure prevention using probability analysis, Wei-
bull plot, Monte Carlo simulation, Markov models, root cause
analysis and other reliability models that heavily depend on
probabilistic methods. MTBF, reliability and availability are all
related to maintenance decision-making criteria, of which the top-
down classication is established, including risk rank, failure
frequency, and safety level dened in RAM standard (EN50126-1,
2006). With RAM indicator and dynamic risk rank of equipment
taken into consideration, failure frequency and their corresponding
failure consequence can be reduced dramatically (Eti et al., 2007).
The biggest challenge for the China petrochemical enterprise to
establish Intelligent Maintenance Indicator Decision-making modelis the lack of historical failure data and maintenance data (Rodney,
2001). From the stance of the factory and equipment, this decision-
making model should make full use of dynamic risk rank indicator,
PMIS indicator and RAM indicator to evaluate the performance of
equipment, so that managers can formulate strategies and make
decisions by collecting, retrieving and analyzing all the informa-
tion. Measures should be focused more on high-risk equipment to
make the proactive maintenance task more efcient and effective.
Equipment failures are often caused by inadequate maintenance
and inability to predict incipient failure. PMIS can automatically
diagnose and predict failure and provide a foundation for decision-
making, such as recommendations for PM, spare parts and main-
tenance tools. Failure prediction and failure prevention is impor-
tant to ensure the stable operation of equipment, and thus
predictive maintenance can boost safety, quality and availability in
the process industrial plants (Carmen Carnero, 2006).
3.1. Reliability data and maintenance data for equipment
Equipment integrity management system has created a program
that collects reliability data and maintenance data through the
archive management workow. The probabilistic analysis, failure
consequence analysis, quantitative risk analysis, failure prediction,
failure prevention, maintenance task optimization and quantitative
indicators of performance monitoring heavily depend on reliability
data and maintenance data, so it will be the foundation of Main-
tenance Indicator Decision-making management to establish
standards for collection, recording, saving and exchanging of these
two types of data. In process industries, reliability data usually
includes the failure mode, failure cause, failure description, failure
position, failure consequences (e.g. safety consequence, economic
consequence, environmental consequences) and failure detection
method, while maintenance data mainly refers to the time when
potential failure is detected, when failure starts, when downtime
begins, when maintenance begins and when maintenance ends.
Both reliability data and maintenance data are often used in
probabilistic analysis, RCA, Weibull plot, Monte Carlo simulation,Markov model and so on to perform failure prediction, reliability
prediction and maintainability prediction.
As Fig. 4 shows, ti represents operation time, t0i represents
repair time, andTirepresents breakdown time. Provided that there
occurs N0 failures during operation and the equipment can
continue to be used as new one after repair. MTBF, MTTR and MTBO
can be calculated by Eqs.(1)e(3) respectively.
MTBF 1
N
XN0i 1
ti (1)
MTTR
1
NXN0
i 1 t0i (2)
MTBO 1
N
XN0i 0
Ti (3)
MTBF is related to availability, reliability and failure frequency,
which represents the number of accidents that occurred in a xed
interval of time. Failure consequence is studied from three aspects
such as the safety consequence, environmental consequence and
economic consequences, which is affected by failure consequence,
while the economic cost is proportional to MTBO.
3.2. RAM indicators
Petrochemical, chemical, rening, petroleum and other process
industrial plants are trying to implement risk-based maintenance
programs to improve safety, reliability and availability of the plants
(Warburton et al., 1998). RAM is one of the risk evaluation models
that are applied in MSI.Whether the implementation and utilization
of RAM indicator-based maintenance programs can be successful
Down
Time
Billing
Time
Maintenance
Start Time
Maintenance
Completion Time
Uptime
Ti
t0i
Down
Time
ti
Potential Failure
Detected TimeFault
End Time
P-F
Fault
Start Time
Fig. 4. Reliability data and maintenance data for equipment.
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heavily depends on the accuracy of the probabilistic models and
reliability data. Equipment failure mayimpact productivity and gross
prot (Warburton et al.,1998), which makes engineering solutions to
expense reduction, reliability enhancement and customer require-
ment satisfaction very important (Barringer, 2000).
3.2.1. Reliability indicator
Reliability, a probabilistic measure of the failure-free operation,
is the probability of the equipment functioning without failure
during a given time period under certain conditions (Kumar,
Klefsjo, & Kunar, 1992), which is often expressed as Eq.(4). It can
be improved by reducing failure frequency.
Rt exp
ti=MTBF
explti (4)
where l is a constant dened as the failure frequency.
Reliability determines whether the output of the plant is as
expected or whether the business can be protable, so it is of great
concern in terms of engineering application, and it helps determine
what andhow much maintenance shouldbe carried out. Equipment
with a long failure-free period can reduce accessories reserves and
maintenance cost. High-reliability can increase equipment avail-
ability while decreasing outrage time, maintenance cost and
secondary failure loss, and thus contribute huge benet for the
company. The key indicators which describe reliability include
MTBF, MTTF, mean life of components, failure frequency, maximum
number of failures permitted in a specic time-interval and so on.
3.2.2. Availability indicator
Availability is dened as the ability of equipment functioning
well during a denite period or even beyond it. It gives an indica-
tion of availableworking time during operation (Kumaret al.,1992),
and can be expressed as in Eq. (5).
Availability MTBF=MTBF MTTR (5)
Increasing failure-free time and decreasing downtime can enhance
availability, which can be converted into reliability and maintain-
ability requirements in terms of acceptable failure frequency and
outage hours.
3.2.3. Maintainability indicator
Maintainability is theability thatequipment canrestore to normal
functionin a speciedperiodof time orbeyondit (Kumar et al.,1992).
It correlates with design and installation quality. Maintainability
indicator can be usedto evaluate, ascertain and explain maintenance
programs and requirements. Maintenance project, personnel, orga-
nization, preparation and procedures all affect maintainability,
which is often expressed in Eq. (6). Designed maintenance proce-
dures and maintenance time are the baseline of maintainability, and
the keygure-of-merit for maintainability is MTTR.
Mt 1 exp
t0i=MTTR
(6)
The shorter MTTR is, the higher the maintainability will be. Three
main parameters: repair time (which is the function decided by
equipment design, and it is related to the training and skill of the
personnel in charge of maintenance), logistic time (i.e. time for
supplying parts) and administrative time (a function of operational
structure of the organization, standard maintenance procedure,
and maintenance quality assurance document) are concerned with
downtime.
High availability, reliability and maintainability and excellent
performance are characteristics of highly effective management,
and they are main indicators of lowering safety cost, environmental
cost and economic cost.
3.3. Equipment dynamic risk rank indicator
Theimportance of equipment may be represented by risk rank. In
general, the risk of equipment in process industry is studied in terms
of safety risk, environmental risk and economic risk, and it is con-cerned with the failure frequency, failure consequence, risk matrix
and risk criterion established according to management goals. Safety
risk rank is determined by safety consequence, failure frequency,
safety riskcriteria andsafety risk matrices; environmental riskrank is
determined by environmental consequence, failure frequency, envi-
ronmental risk criteria and environmental risk matrices; economic
risk rank is determined by economic consequence, failure frequency,
economic risk criteria and economic risk matrices. The criteria
which are related to reliability, availability and maintainability are
mainly dened by engineers, maintenance staffs, safety authorities.
Safety risk (Rs) is the product of safety probability of failure
(PoFs) and safety consequence of failure (CoFs), and it is calculated
by Eq.(7).
RS PoFS CoFS (7)
where PoFs is calculated from the failure frequency and CoFs is
specied by maintenance data from archive management module.
Environmental risk (RE) is the product of environmental prob-
ability of failure (PoFE) and environmental consequence of failure
(CoFs), and it is calculated by Eq. (8).
RE PoFE CoFE (8)
where PoFE is calculated from t failure frequency and CoFE is
specied by maintenance data from archive management module.
Economic risk (RC) is the product of economic probability of
failure mode (PoFC) and economic consequences of failure (CoFC),
and it is calculated by Eq. (9).
RC PoFC CoFC (9)
where PoFC is calculated from failure frequency and CoFC is speci-
ed by maintenance data from archive management module.
Economic cost mainly comes from production loss due to outage
time (Ti) and maintenance cost due to equipment failure (ti).
Equipment risk rank is dened by the highest risk rank of all risk
ranks corresponding to failure modes of equipment. Suppose the
risk rank of theith failure mode isRi, it is derived from Eq.(10).
Ri MaxRSi; REi; RCi (10)
Then the risk rankR is derived from Eq.(11).
R MaxRi; i 1; 2; 3; .; n (11)
In some cases, risk criteria are certain, so the main inuence
factors to dynamic risk changes are the failure frequency and failure
consequences, while failure frequency, also be called failure rate, is
usually more important. On one hand, the dynamic risk rank
indicator is an effective way of evaluating the previous risk rank
and inspection/maintenance task; on the other hand, it lays the
foundation for managers to revise management objectives and
establish the next risk evaluation task.
If the failure mode is identied, the risk is evaluated by
analyzing failure frequency, failure consequence and failure
detectability. If the risk is too high, efforts are needed either to
reduce the frequency and/or consequence, or to increase failure
detectability in order to make it possible to avoid or at least to
reduce the severity of the failure.
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management, abnormality management, operation manage-
ment, archives management, work identication and optimi-
zation. Inspection/maintenance tasks derived from risk
management are implemented through professional manage-
ment, which aims to ensure the operational quality and stan-
dard of inspection/maintenance management, and it is also
called the quality assurance workow procedures. Optimized
maintenance tasks (e.g. preventive tasks, predictive tasks, etc.)
derived from risk management are re-identied and conrmed
by maintenance experts. Various maintenance tasks which
include content, maintenance program, repair les and so on
are examined, conrmed and optimized by different adminis-
trative roles in the professional management workow.
Maintenance tasks are implemented by the PM module in ERP,
while reliability data and maintenance data are collected and
saved through PM02 maintenance orders and retrieved
through the interface between SOA and PM module. After
veried through archive management workow, the two types
of data are stored in the archive management module of MSI.
From maintenance strategies to maintenance orders, risk
Fig. 5. Failure symptom degradation trend can forecast equipment fault. Lines 1, 2, 3 and line 4 represent the rotor unbalance, the shaft misalignment and the bearing fault
degradation trend line respectively.
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management has been successfully combined with profes-
sional management.
(6) Dynamic risk evaluation. Dynamic risk evaluation is based on
risk rank that is determined by the highest risk rank of
equipment. Risk rank evaluation is conducted in terms of
safety, environment and economy, and it is affected by failure
frequency, failure consequence and risk criterion. If the risk
criterion is determined, the failure frequency and the failure
consequence, which are both key performance indicators of
reliability data, will determine risk rank. These two factors are
derived from the historical inspection/maintenance database
in the archive management module, and due to the changes of
historical maintenance data, risk rank will change accordingly
with those of failure frequency and failure consequence.
From RAM and risk analysis, equipment reliability or avail-
ability which falls short of expectation will be identied and the
weakness or failure will be eliminated. Equipment with more
failure frequency, long repair time or high degree of uncertainty
can be singled out. Maintainability analysis has been used
to evaluate the design and layout with respect to maintenanceprocedures and maintenance resources. Availability goals can be
converted into reliability and maintainability requirements by
means of acceptable failure frequency and outage hours for
equipment.
4. Training
MSI is a newequipment management model, and it involves risk
management and professional management such as RBI, RCM, SIL,
key device identication, key device classication, predictive
maintenance, condition monitoring, abnormality management,
data collection, data exchange, data storage, archive management,
lubrication management, operation management, failure identi-
cation, task optimization and so on. Personnel in charge of equip-
ment management have accustomed to traditional corrective
maintenance management culture. On the one hand, they want to
be able to jump out of the circle ofre-proof management mode,
and they wish equipment reliability, availability, maintainability
and safety can be promoted; on the other hand, due to the lack of
professional management knowledge and skills such as risk
management, reliability data collection, predictive maintenance,
probability analysis and reliability prediction, it is very difcult for
them to accept, master and apply novel equipment management
models. To make personnel at all levels understand and master MSI
better, training is very important, and the training content for using
MSI primarily include as follows:
(1) (Probabilistic) risk assessment technology, such as RCM, RBI,
SIL evaluation, Risk-based maintenance, probability analysis,
RAM statistical analysis and reliability prediction.
(2) The collection and exchange of reliability and maintenance
data for equipment technology, which involves data collection,
data exchange, data storage and archive management.
(3) Predictive maintenance technology, such as the fault symptom
signals detection, fault trend analysis & prediction, failure
diagnosis and so on.
(4) Intelligent equipment maintenance indicator decision-making
technology and the inspection/maintenance tasks formulating
technology.
(5) Equipment maintenance and safety integrity management
technology, such as identication and classication of critical
equipment, inspection and preventive maintenance, abnor-
mality management and so on.
International experiences show that the key factors affecting the
successful transition to a more risk-informed approach include rm
support from both the manager and the engineers as well as
education and training for engineers, operators and maintenance
staffs (Andrew, & Toshihiro, 2007). From the application point of
view, the transition involves reform in management programs and
management culture, which need to promote and establish
equipment management system, management procedures and
management culture in relation to MSI. The reform involves
working model and working skills. The popularization and appli-cation of MSI cant be nished at once, but needs continuous and
industrious improvement. Without the support from top leaders,
managers at all levels and staffs, the best equipment maintenance
and safety management decision-making model will fail (Jesus,
Jose, & Felix, 20 03).
Fig. 6. Inspection and maintenance task package formulating process, maintenance strategy derives from the intelligent equipment maintenance indicator decision-making model:
equipment dynamic risk rank indicator (2 M), RAM indicators (4 M), predictive maintenance indicator (5 M).
Table 1
Maintenance resource allocation proportion: optimizing maintenance model (using MSI) compared with traditional model (unused MSI).
Maintenance mode Breakdown
maintenance (%)
Preventive
maintenance (%)
Predictive
maintenance (%)
Risk-based inspection/maintenance MSI
Traditional 67 32 1 Unused Unused
Optimized 37 30 33 Being Applied Being Applied
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5. Applications case
In this study, MSI is developed in support of Jinzhou Petro-chemical Company, which aims to investigate a kind of mainte-
nance and safety management model that can help petroleum,
chemical, petrochemical and gas plants to improve reliability,
availability and safety for equipment as well as promoting the
equipment information management level. Using real-time data-
base, web service and SOA technology, a data framework is devel-
oped to provide a unied data structure and manemachine
interface. Meanwhile, this framework is integrated with process
data, condition monitoring data, historical inspection/maintenance
data, historical failure data and dynamic risk evaluation data to
support prediction and comprehensive analysis. As a result,
personnel at all levels can grasp equipment condition timely and
accurately in order to make maintenance and safety management
decision-making more effective and focused.The Jinzhou Petrochemical Company is a traditional rening and
chemical plant with a history of more than 70 years, breakdown
maintenance and preventive maintenance account for 67% and 32%
respectively, and predictive maintenance accounts for no more
than 1%. CMMS, MES and ERP have all been established and applied
in the plant, but they are isolated from each other and thus forming
Information Island due to the lack of unied interface and standard
reliability and maintenance data for exchange. Due to the lack of
identication and classication of critical equipment, it is very
difcult to rational allocation of maintenance resources only based
on empirical and qualitative approach. There exists some certain
maintenance deciency, maintenance surplus, high security hidden
danger and too many accidents. The foremost tricky problem is the
lack of historical data. Without them, we cant establish a quanti-
tative risk rank method.
Firstly, we established MSI system based on SOA as shown in
Fig. 3, which realized seamless connection in all function modules
and systems such as the risk evaluation tool, predictive mainte-
nance module, archive management module, operation manage-
ment module, lubrication management module, abnormality
management module, work identication and prioritization
module, MES and ERP system. An information collection and storage
system that can provide needed data for analysis is essential.
Secondly, data collection, data exchange and data storage
standards of both reliability data and maintenance data are estab-
lished according to ISO 14224:1999, so are the failure classicationand failure coding standards, making interconnection among all
function modules possible. Reliability data, maintenance data,
process data, dynamic monitoring data and archive data may be
retrieved from ERP, MES and PMIS, and quantitative risk rank
evaluation is established.
Thirdly, RAM indicator model, predictive maintenance indicator
model and equipment dynamic risk ranking indicator model are
established separately in support of maintenance decision-making.
After more than 1 years operation, failure modes, failure
frequency, failure consequence, MTBF and other reliability and
maintenance data for equipment are stored in the system, and
these data provide the data source for dynamic quantitative risk
evaluation and maintenance decision-making. At the same time,
failure frequency and failure consequences reduced, and mainte-nance resource allocation is optimized.
Table 1 shows that maintenance management mode and
resources allocation has experienced a dramatically change after
Table 2
Total rotating equipments risk rank evaluation (RCM) report for the Jinzhou Petro-
chemical Company.
Name
code
Equipment
category
Equipment
quantity
Risk level distribution
High (%) Medium (%) Low (%)
1 Reciprocating Compressor 41 41.50 58.50 0
2 Centrifugal Compressor 28 100 0 0
3 Screw Compressor 24 25 75 0
4 Centrifugal Pump 1692 2.80 32.50 63.705 Reciprocating Pump 129 0 27.20 72.80
6 Gear Pump 54 0 0.00 100
7 Screw Pump 20 0 30 70
8 Liquid Ring Pump 12 0 0.00 100
9 Roots Blower 11 0 0.00 100
10 Centrifugal Fan 122 0 0.00 100
11 Axial Flow Fan 225 0 19.10 80.90
12 Steam Turbine 8 100 0.00 0
13 Flue Gas Turbine 3 100 0.00 0
14 Generator 7 100 0.00 0
15 Others 426 0 0 100
Failure
Freq
uency
Recipro
cating
Compr..
.
Centrif
ugalC
ompre
ssor
Screw
Comp
ressor
Centrifu
galPum
p
Recipro
cating
Pump
GearPu
mp
Screw
Pump
Liquid
RingP
ump
RootsB
lower
Centrif
ugalFa
n
Axial
FlowF
an
Steam
Turbin
e
FlueG
asTurb
ine
Genera
tor
Equipment Category
Unused MSI
Using MSI
Fig. 7. Failure frequency comparative analysis report for typical rotating equipments (using MSI compared with unused MSI).
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exploiting MSI. Risk-based inspection/maintenance is introduced
and applied in Jinzhou Petrochemical Company. Breakdown
maintenance resource expense of utilizing MSI compared with
traditional model has decreased 30%, and the predictive mainte-nance resource cost has increased 32% accounts for 33%.
Table 2describes the RCM evaluation results, the risk has been
classied into three rank levels (High, Medium and Low) according
to the quantitative analysis and calculation of risk evaluation, and
risk level distribution varies with equipment category. All centrif-
ugal compressors, steam turbines, ue gas turbines and generators
are high-risk equipments. And the high-risk equipments propor-
tion of reciprocating compressors, screw compressors and centrif-
ugal pumps account for 41.5%, 25% and 2.8% respectively.
Risk level is determined by equipment failure frequency, failure
consequence, risk matrix and risk criterion, and the best way to
lower risk of equipment is to execute inspection/maintenance tasks
formulated on the basis of risk evaluation. It can be seen from Fig. 7,
the failure numbers per year for reciprocating compressors,centrifugal compressors, steam turbines, ue gas turbines, gener-
ators and other high-risk equipments have reduced signicantly.
Table 3 demonstrates that the utilization MSI has a positive
effect on improving equipment reliability, availability and utiliza-
tion compared with traditional maintenance model (unused MSI),
and this can reduce the safety accidents which result from equip-
ment failure.
Predictive maintenance decision-making indicator of MSI plays
an important role on formulating optimum strategy that antici-
pates, avoids and eliminates problems and maintenance. Alongwith
RCM, RBI and SIL evaluation tools, PMISproveseffectively to identify
and eliminate defects, minimize and avoid failures, minimize
breakdown maintenance and maximize predictive maintenance.
6. Conclusions
This study emphasizes the importance of the integrating risk
management with professional management, and investigates the
necessity of applying MSI in process industry, which can guarantee
the failure-free operation of equipment in Jinzhou Petrochemical
Company. The application results suggest that the established RAM
indicator model, predictive maintenance indicator model and
dynamic risk rank indicator model should be considered in main-
tenance decision-making and maintenance planning in order to
increase reliability, availability, maintainability and safety of
equipment. In order to ensure the efciency and effectiveness of
inspection/maintenance, some professional management work-
ow such as lubrication management, abnormality management as
well as work identication and prioritization have been set up.
Based on principles of equipment integrity management in process
industry, MSI has realized PDCA Cycle: the formulation of the
inspection/maintenance program (Plan), implementation ofinspection/maintenance tasks (Do), performance check (Check)
and information feedback (Adjustment).
Maintenance standards, reliability data and maintenance data
are insufcient or even unavailable, while data exchange standards
for data collection and data exchange are not unied. Many
equipment management systems such as ERP, CMMS, EAM and
MES are isolated from each other, thus forming Information Island.
Enterprise management usually lacks comprehensive skills in
terms of risk management, probabilistic analysis, predictive main-
tenance and so on, and this hinders the successful application of
MSI. Engineering practice shows that support from the manage-
ment, improvement of equipment integrity management system,
promotion of management procedure as well as sustained training
and education are critical to the successful application of MSI. Thepilot application of this system in Jinzhou Petrochemical Company
shows that MSI can be built on the basis of traditional equipment
management model and risk evaluation together. RAM indicator
model, predictive maintenance indicator model and dynamic risk
rank indicator model are established to support maintenance
decision-making, which is actually benecial to the improvement
of reliability, availability, maintainability and safety of equipment,
and it can help optimize the maintenance resource scheme.
Acknowledgements
The authors would like to acknowledge the support of the
National Key Technology R&D Program (Approved Grant No.
2006BAK02B02) and the Scientic Research Foundation of Grad-uate School of Beijing University of Chemical Technology innova-
tion (Approved Grant No. 09Me003).
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