Integrated Maintenance System Trend and A Maintenance ... · A Maintenance Scheduling System ... 8 Trends of Maintenance Methods ... when a machine turns to maintenance condition,

© Hitachi, Ltd. 2012. All rights reserved.

HITACHI CONFIDENTIAL

2012.11.05

Youichi NONAKA, Yuuichi SUGINISHI, Toshiharu MIWA Yokohama Research Laboratory, Hitachi,Ltd.

Japan

Integrated Maintenance System Trend and

A Maintenance Scheduling System Application

2

Power Plants

Loading Shovel(80t)

Construction Machinery

Disk Array Subsystem

Storage Systems

British CTRL Train

Transportation

CTRL: Channel Tunnel Rail Link

Open MRI System

Medical Equipments Building Systems

Intelligent Elevators

Hitachi Products

Combined Cycle Plant

HITACHI Yokohama Research Lab.

System/service Autonomous

decentralization

Mathematical engineering

Security

Support fusion business by info./telecomm. and manufacturing tech.

IT platform

Storage

Middleware

Network

Embedded system System

engineering

Digital image application

Embedded Linux

Manufacturing Production system

Processing/packa-ging/assembling

Inspection/mea-suring

Improving/totally optimizing by effective utilization of Big Data

Social infrastructure business

Highly-reliable cloud construction led by storage system

Info./telecomm. business

ｘ

Fusion Business

Info.: information Telecomm.: telecommunication

3



１．Integrated Maintenance System Trend

２．Maintenance Scheduling System Application

Contents





Contents

$

Reference: Kim, S. et al, Intl. journal of services technology and management,Vol.3(2010)

It is reported that O&M(operation & maintenance) revenue such as service and parts replacement during product life-cycle exceeds sales revenue in aircraft engine industry.

Maintenance is a key activity to prevent from the stoppage due to machine failure.

However, maintenance causes the decrease of working ratio…

Impact of Maintenance on Revenue 6

Time

Functional Level

A. Improvement B. Preventive Maintenance

Failure Initial Malfunction

C. Breakdown Maintenance

D. Upgrade

Required Functional Level Degradation

Change of Required Functional Level

RUL

Prognostics Condition Monitoring

RUL： Remaining Useful Life

Various Maintenance Activities 7

Maintenance methods become advanced with the progress of condition monitoring and failure diagnosis technologies utilizing information and communication technology.

Trends of Maintenance Methods 8

Breakdown Maintenance

Preventive Maintenance

Time Based Maintenance

Condition Based

Maintenance

Prognostic and Health

Management

Advantage Easy to Operate Improvement of Maintenance Efficiency Due to Proactive

Action

Challenge Avoidance of Critical Stoppage

Difficulty of Appropriate

Maintenance Timing

Need of Practical Failure Detection / Prediction Method

Op.: Operation, Maint.: Maintenance, O&M: Operation and Maintenance :Product / Equipment

1

2

3

Maintenance

Mobility

O&M Type

Distributed

Centralized

No. Example of Business Situation

Site User

Op. / Maint. •Power Plant

•Manufacturing Line

•Construction Machine for Mine

Site

User/ Maker

（on-site）

Site

Site

Op.

Op. Op.

Maint. •Elevating Machine

•Medical Equipment

•Electronic Device

User

User (Operation Company)

Maint.

Maker (Dispatch & On-site)

User/ Maker

(Maintenance Station)

Op.

Op.

Op.

Station

Operation

Requirement of Maintenance Scheduling 9 Each O&M type requires different scheduling approaches

•Railway

•Aircraft


1

2

3 Mobility

O&M Type

Distributed

Centralized

No.


Previous Study

Proactive scheduling method is developed to improve the operation ratio and productivity since operations are complicated and timing of maintenance is flexible in the production line (Nonaka, 2010).

Remote monitoring system of products distributed all over the world is introduced in order to manage the maintenance scheduling and parts inventory efficiently using remote and real-time monitoring data (Xiang,2011).

The adaptive and heuristic scheduling method is applied to quickly find new suitable vehicle movement plans and reconstruct maintenance packages with as few maintenance stops as possible while keeping the risk of breakdowns low (Bohlin,2008).


1

2

3 Mobility

O&M Type

Distributed

Centralized

No.


Previous Study

Proactive scheduling method is developed to improve the operation ratio and productivity since operations are complicated and timing of maintenance is flexible in the production line (Nonaka, 2010).

Remote monitoring system of products distributed all over the world is introduced in order to manage the maintenance scheduling and parts inventory efficiently using remote and real-time monitoring data (Xiang,2011).

The adaptive and heuristic scheduling method is applied to quickly find new suitable vehicle movement plans and reconstruct maintenance packages with as few maintenance stops as possible while keeping the risk of breakdowns low (Bohlin,2008).





Contents

Power Plants Storage Systems Transportation

Target Products & Process 13

Manufacturing System Materials

Parts for Products

Characters of Manufacturing System 14

Manufacturing System Materials

Parts for Products

Process A

Process B

Process C


Process A Process B Process C

Step4

Group A Group B Group C

Materials

Parts

Step5

Step1 Step2 Step3

Step6 Step7

1.Target manufacturing system is “re-entrant type”, in which process routing goes through same process repeatedly.

2.Each process in the system has machine group. In the group, one machine owes one process step at the same time.




3.In one product case, the process routing consists of hundred process steps, and manufacturing lead time takes several weeks.

4.Another case has multiple part type, so various process routings are defined to the manufacturing system.

Material2

Material3

Part2

Part3

Step4

Material1

Parts

Step5

Step1 Step2 Step3

Step6 Step7

Machine Maintenance Problem 17



5.In this system, when a machine turns to maintenance condition, some process steps would be disturbed its work by the maintenance.

6.This situation affects the system throughput and manufacturing lead time.

In maintenance

Step4

Materials

Parts

Step5

Step1 Step2 Step3

Step6 Step7

Disturbed by maintenance

Maintenance Type 18



In maintenance

Step4

Materials

Parts

Step5

Step1 Step2 Step3

Step6 Step7


1. Preventive Maintenance

a. Time-Based Maintenance

b. Condition-Based Maintenance

2. Breakdown Maintenance

Today’s Topic

Our Approach: S-Model Based Production System S-Model: Statistical Model 19


Group A Group B Group C In maintenance

Step4

Materials

Parts

Step5

Step1 Step2 Step3

Step6 Step7


Statistical Information Extraction

Operation Management

Manufacturing Logs Acquisition

Manufacturing Simulation

Manufacturing Prediction & Pre-recovery Planning with Statistical Methods and Simulation Technologies

Mathematical Model

1min – 1 day /cycle

6 hour – 1 day /cycle automatically

S-Model Based Production System S-Model: Statistical Model 20



Step4

Materials

Parts

Step5

Step1 Step2 Step3

Step6 Step7






1. Manufacturing Condition Prediction 2. Pre-recovery Planning

Mathematical Model






Step4

Materials

Parts

Step5

Step1 Step2 Step3

Step6 Step7







Mathematical Model



Manufacturing Condition Prediction Method 22

Multi-agent based manufacturing data mining technology breaks huge amount of

manufacturing actual data mining calculation for manufacturing condition prediction

Simulation Data Base

Manufacturing Actual Data Base

Report Agent

Data Mining Agent

Simulator for Manufacturing

Condition Prediction

TSUNAMI

Over 1M records/day

23

cix-1

Pn . . .

Pi+1 Pi Pi-1 . . .

P2 P1 . . .

Tx

Tx+1

Tx-1

. . .

. . .

cix

cix+1

. . .

ci-1x-1

. . .

ci-1x

ci-1x+1

. . .

ci+1x-1

. . .

ci+1x

ci+1x+1

. . .

. . .

. . .

. . .

. . .

. . .

. . .

. . .

. . .

. . .

. . .

c2x-1

. . .

c2x

c2x+1

. . .

c1x-1

. . .

c1x

c1x+1

. . .

cnx-1

. . .

cnx

cnx+1

. . .

Time

Process Chain

cix = { Moving Average of Performance Indicator

at Process i Time x with a period }

{ Variance of Performance Indicator at Process i Time x with a period }

Performance Indicator: WIP, Throughput, Cycle Time, Yield, etc.

Visualization Coefficient

Manufacturing Condition Prediction Method

24

cix-1

Pn . . . Pi+1 Pi Pi-1 . . . P2 P1

. . .

Tx

Tx+1

Tx-1

. . .

. . .

cix

cix+1

. . .

ci-1x-1

. . .

ci-1x

ci-1x+1

. . .

ci+1x-1

. . .

ci+1x

ci+1x+1

. . .

. . .

. . .

. . .

. . .

. . .

. . .

. . .

. . .

. . .

. . .

c2x-1

. . .

c2x

c2x+1

. . .

c1x-1

. . .

c1x

c1x+1

. . .

cnx-1

. . .

cnx

cnx+1

. . .

Time

Process Chain

Type

A

B

C

Class

LB < < UB

cix

cix

cix

< LB . . .

. . .

. . .

UB <

=

=

Color

Area that has ignorable fluctuations

Supposed Condition of Material Flow

Focused area that fluctuations tends to occur

Special area that has to be stabilized

LB; Lower Boundary, UB; Upper Boundary

Some patterns are composed reflecting

supply fluctuation propagation

Manufacturing Condition Prediction Method

Process Chain

Mfg System Shut Down & Resumption

Tool Performance Fluctuation

TSUNAMI of WIP

• Special clusters was generated by tool performance fluctuations at the system resumption

• Accumulated WIP is transferred into the downstream

25 Manufacturing Condition Prediction Method

Manufacturing Logs


Future

Past

Process Chain

cix-1

Pn . . . Pi+1 Pi Pi-1 . . . P2 P1

. . .

Tx

Tx+1

Tx-1

. . .

. . .

ci-1x-1

. . .

ci+1x-1

. . . . . .

. . .

. . .

. . .

c2x-1

. . .

c1x-1

. . .

cnx-1

. . .

Now



Propagation Prediction

cix

cix+1

. . .

ci-1x

ci-1x+1

. . .

ci+1x

ci+1x+1

. . . . . .

. . .

. . .

. . .

. . .

. . .

c2x

c2x+1

. . .

c1x

c1x+1

. . .

cnx

cnx+1

. . .

Manufacturing Logs


Future

Past

Process Chain

cix-1

Pn . . . Pi+1 Pi Pi-1 . . . P2 P1

. . .

Tx

Tx+1

Tx-1

. . .

. . .

ci-1x-1

. . .

ci+1x-1

. . . . . .

. . .

. . .

. . . c2x-1

. . .

c1x-1

. . .

cnx-1

. . .

Now




Maintenance Impact Study

Process Chain

Down Period

Time

• A tool down for maintenance is simulated in the former part of the process routing, and its down period is indicated at the time axis

•In the figure, several clusters can be seen composed by yellow and red

•Most of small clusters would occur by tool process time fluctuation, but some clusters are recognized as TSUNAMI

•Some of large TSUNAMI occurred from the down period

28

Machine Maintenance

Down for Maintenance


Process Chain

Down Period

Time

29

Machine Maintenance

Oscillation

Pendulum Swing Phenomenon

• Along with time axis, TSUNAMI occurs not only by tool down directly, but also by the aftermath like an oscillation of pendulum swing

• Once large TSUNAMI occurs, production planning and scheduling should pay attention to this phenomenon


Process Chain

Down Period

Time

30

Machine Maintenance

Stemming Phenomenon

•Along with process flow axis, some bottleneck processes stem TSUNAMI

•Even when a large TSUNAMI occurs, latter portion of the process routing might have no TSUNAMI Effect if a bottleneck tool is placed between TSUNAMI and the latter portion

Stemming Point

Down for Maintenance

31

Manufacturing System Size •Num of Process: About 1000 - 1300 •Num of Tools: About 400 •Num of Workers: About 150 •Frequency of Manufacturing Logs: About 1M Items/Day

Process Chain

Now

Machine Maintenance


Logs Visualization

Tim

e

Result of Manufacturing Condition Prediction

Demonstration

Our Approach: S-Model Based Production System S-Model: Statistical Model 33



In maintenance

Step4

Materials

Parts

Step5

Step1 Step2 Step3

Step6 Step7







Mathematical Model



34

Time

Time-Based Maintenance Problem

1

2

3

Pro

cess

Ste

p

Vertical line indicates the process steps of the system, and horizontal like indicates time line. Therefore, production works moves from left top to right down along with green arrows.

a b c d Production Work

35

Time

1

2

3

Pro

cess

Ste

p

Maintenance

Delay

Time-Based Maintenance Problem

If the machine of process step 2 turns to maintenance condition, the maintenance schedule will disturb work b process, and that leads to manufacturing lead time delay.

In maintenance

a b c d

36

Time

1

2

3

Pro

cess

Ste

p

Maintenance

Findings of Maintenance Characters

a b c d

This study found almost all maintenance works have some margin between the maintenance scheduling period and the real maintenance work in the period.

Production Work

Maintenance schedule margin

37

Time

1

2

3

Pro

cess

Ste

p

Production Work

Concept of Proactive Scheduling for Maintenance (PSM)

Maintenance

So our approach optimizes both maintenance work schedules and production work schedule to avoid conflicts each other, even if the system has hundreds of process steps and various work types as “re-entrant type” system.

Slide

38 PSM Numerical Model

Period of Maintenance Plan

Ei,k-1 Bi,k tj

Job k-1 of Ai,s

Time Si,k+1

Job k+1 of Ai,s

Maintenance Job k

Ei,k

Maintenance Job k

Si,k

Target Lot Job r

S*i,k

E*i,k

Target Lot Job r

t*j

Slide Back of Maintenance Job

Slide Forward of Lot Job

A model in Machine i, Process s

39 PSM Overall Algorithm

Step.1 Initialization

Step.2 Scheduling time count up

Step.3 Lot selection for scheduling

Step.4 Process selection of the lot

Step.5 Candidate machine selection

Step.6 Schedule allocation by PSM model

Start

Time end

No lot

All process end

End

•This overall algorithm can not convince an optimal schedule for entire manufacturing lead time reduction, since this algorithm modifies the priority of production lot from input process to final process "by process".

•Therefore, PSM takes the multi-start method and the greedy method, to the selection criteria for both production lots and machines.

40 Multi-start Greedy Algorithm for PSM Optimization

Lj = {Lx | min(RDx / RWx) e-αC + RN0-1}

•Multi-start algorithm application for lot selection

Lj: Selected production lot

RDx: Time remaining until deadline date as of scheduling time t

RWx: Process time remaining as of scheduling time t

RN0-1: Random number between 0 and 1

α: Weighting coefficient applied to increase randomness

C: Number of starts

β < Ns

•Greedy algorithm application for machine selection

β: Threshold value

Ns: Number of machines in the job shop responsible for process s

When this formula is broken, then the procedure stops machine allocation and return to lot selection step.

41 Performance Verification of Multi-start Greedy Algorithm for PSM Optimization

Item

Method

Ratio Lagrangian Decomposition Coordination

Multi-start Greedy

Calculation Time (sec) 2491 3 830.2

Manufacturing Lead Time (min) 3604 3604 1.0

Total Slide Time (min) 6654 5241 1.3

Num of Slide Machine 2 1 2.0

1.Multi-start Greedy method and the Lagrangian Decomposition Coordination algorithm obtained the same result for the manufacturing lead time of the prototype lot

2.But Lagrangian Decomposition Coordination took approximately 830 times as much calculation time as Multi-start Greedy approach.

3.Lagrangian Decomposition Coordination-generated plan required rescheduling of two machines, while Multi-start Greedy method-generated plan only required rescheduling one machine.


1.While Lagrangian decomposition coordination method rescheduled maintenance for Machine No.40 and 79, Multi-start Greedy method only rescheduled Machine No.79.

2.Additionally, the slide volumes were lower in the latter method.

Maintenance No.

Machine No.

Slide Volume (min)

Lagrangian Decomposition Coordination

Multi-start Greedy

1 14 0 0

2 27 0 0

3 40 697 0

4 44 0 0

5 79 5957 5241


1.In the initial condition, an approximately 12,000-minute interference exists between the prototype lot schedule and the maintenance schedule for Machine No.79.

6000 9000 12000 15000 18000 21000 24000 27000 Scheduling Time (min)

Initial Condition

Idling Lagrangian Decomposition

Coordination

Maintenance Schedule Production Work Schedule

Conflict

Multi-start Greedy

Rescheduled Maintenance


2.Lagrangian Decomposition Coordination result shows a gap between the prototype lot schedule and Machine No.79 maintenance schedule, which represents wasted time.

3.Multi-start Greedy method result assigns a maintenance schedule to Machine No.79 just after the prototype lot schedule.

6000 9000 12000 15000 18000 21000 24000 27000 Scheduling Time (min)

Initial Condition

Idling Lagrangian Decomposition

Coordination

Maintenance Schedule Production Work Schedule

Conflict

Multi-start Greedy

Rescheduled Maintenance

45 PSM Application Sample

0

5

10

15

20

25

30

Manufacturing Lot Scheduled by PSM

Manufacturing Routing

Manufa

cturing L

ead T

ime (

days)

Scheduled Machine Maintenance

Lot Scheduled by Conventional Approach

4.5days

Num of Processes : Approx.1000 Num of Machines : Approx.400

1. System automatically collects all process start and finish times for all lots, so these data were collated to obtain the manufacturing lead times for each process.

2.In this sample, PSM schedules were provided to all of the offices in charge of machine maintenance

Main

tenance W

ork

Main

tenance W

ork

46 U

tiliz

ation R

ate

Work Day Work Day

Before After

15% Up

Example of Asset Utilization Up

Serv

ice R

ate

for

Due D

ate

100

98

96

94

92

90

Before After

Jul Jun Aug Sep May

47

8% Up

Example of Service Rate for Due Date




In maintenance

Step4

Materials

Parts

Step5

Step1 Step2 Step3

Step6 Step7






Manufacturing Prediction & Pre-recovery Planning with Statistical Methods and Simulation Technologies

Mathematical Model



49 Awards of S-Model Based Production System

2007 CIRP Manufacturing System,

Royal Academy of Engineering, Best Paper Award

2009 Japan Electrical Manufacturers' Association, Award

2010 Japan Society for Precision Engineering, Takagi Prize

2011 FANUC FA Robot Foundation, Paper Award

50 Conclusion

1. Integrated Maintenance System Trend was discussed.

• Maintenance Scheduling System was categorized by O&M type: Centralized, Distributed, and Mobility.

2. A Maintenance Scheduling System of Centralized O&M type, S-Model Based Manufacturing System was proposed.

• This system performs manufacturing prediction & pre-recovery planning with statistical methods and simulation technologies.

• With this system, flexible, strong production control system network has been constructing that controls productivity detractor propagation caused by machine maintenance.

Documents

Integrated Maintenance System Trend and A Maintenance ... · A Maintenance Scheduling System ... 8 Trends of Maintenance Methods ... when a machine turns to maintenance condition,