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Inspection Decisions Part 5

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©A.K.S. Jardine

Optimizing Condition-basedMaintenance (CBM) Decisions

CBM Strategies (7.1)Estimating RUL (7.2)

The EXAKT model (7.3)Case Studies (7.4)

Andrew K.S.Jardine

Department of Mechanical & Industrial Engineering

University of Toronto

Canada, M5S 3G8

[email protected] .ca

October , 2002.

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“Smart” Condition Monitoring

1. Engineering Approaches: physics of failure2. SPC Models: Trending3. Expert Systems: Human-computer 4. Neural Networks: Knowledge discovery

algorithms (data mining, pattern recognition)5. Optimization Models: Blending risk and economic

considerations

Reference: A.K.S. Jardine, “Optimizing Condition-Based Maintenance Decisions”, RAMS 2002 , January 28-31, 2002, Seattle, Washington.


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Analysis of Shear PumpBearings Vibration Data

– 21 vibration measurements(covariates) provided byaccelerometer

Using : – 3 measurements (covariates)

significant

A Check: – Had model been

applied to previous histories – Savings obtained = 35 %

Campbell Soup Company

Source: Jardine, AKS, Joseph, T and Banjevic, D, “ Optimizing condition-based maintenance decisions for equipment subject to vibrationmonitoring” , Journal of Quality in Maintenance Engineering, Vol. 5. No. 3, pp 192-202 , 1999.

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APPROACH USEDHAZARD - RISK OF FAILURE(PROBABILITY OF FAILURE)

THAT COMBINES

AGE OF EQUIPMENT ANDCONDITION-MONITORING DATA

USING

PROPORTIONAL-HAZARDSMODEL (PHM)


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“Early work with PHM”

Source: M. Anderson, A.K.S. Jardine and R.T. Higgins, “The use o f concomitant variables in reliability estimation”, Modeling and Simulation , Vol. 13, pp. 73-81, 1982.

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Number Flight Hours Fe Cr Hazard Rate

1 11770 5 6 0.043


1 11770 5 6 0.043


1 11770 5 6 0.043

2 11660 2 6 0.012

3 8460 12 2.4 0.0071

4* 12630 8 1 0.0014

5 7710 8 0 0.00094

6* 9240 2 3 0.000297* 5660 10 1 0.00020

8* 7190 2 2.5 0.000073*Doubtful Removal


1 11770 5 6 0.043

2 11660 2 6 0.012

3 8460 12 2.4 0.0071

4* 12630 8 1 0.0014

5 7710 8 0 0.00094

6* 9240 2 3 0.000297* 5660 10 1 0.00020

8* 7190 2 2.5 0.000073*Doubtful Removal

Estimated Hazard Rate at Failure

The hazard rate equation is:

4.4724100

tr(t)= ( 24100 )3.47 (.41z +.98z )e 1 2

where z 1 is Fe concentration and z 2 is Cr concentration


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DATA PLOT

RISK PLOT Age

Data

Age

Risk

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OPTIMAL POLICY - OPTIMAL RISK LEVEL

Optimalrisk level

Age

Risk

Risk

Cost/unit time

RISK PLOT

COST PLOT

Ignore risk

Replace atfailure only

minimal cost

optimal risk


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CONDITION BASED OPTIMAL REPLACEMENT

OF A PRODUCTION SYSTEM

V. Makis and A.K.S. Jardine

Department of Mechanical and Industrial Engineering

University of Toronto

V. Makis and A.K.S. Jardine, Optimal Replacement in the ProportionalHazards Model, INFOR, Vol. 20, pp 172-183, 1992

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We have created atheory, but in order to

make it work inpractice we need a tool


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Commenced Funding: January 1995Developed by senior researchers, statisticians, andprogrammers at the University of Toronto’s ConditionBased Maintenance Laboratory.Underwritten by the governments of Ontario and Canadaand:

Zachry

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RESEARCH STAFF

PRINCIPAL INSVESTIGATORS:Prof. A.K.S. JardineProf. Viliam Makis

RESEARCH STAFF:Dr. Dragan Banjevic, Project Director Walter Ni, Programmer AnalystMurray Wiseman, Research Associate

Dr. Daming Lin, Post Doctoral FellowDr. Gang Li, Post Doctoral FellowJayne Beardsmore, Administrative/ Research Assistant

RESEARCH STUDENTS:Yan Gao (M.A.Sc ) Tian Tang (M.A.Sc)Yiding Li (M.A.Sc) Jianmou Wu (Ph.D)Bing Liu (Ph.D) Yimin Zhan (Ph.D)Miao Qiang (M.A.Sc) Ali Zuashkiani (Ph.D)


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©A.K.S. Jardine© CBM LABORATORY, UNIVERSITY OF TORONTO

Age DataDiagnostic Data CBM Model

MaintenanceDecisions

CBM OPTIMIZING SOFTWARE

UNIFICATION OF DATA AND DECISIONS

Ref: Banjevic D., Jardine, A.K.S., Makis, V. and M.Ennis., “T he Optimal Control Policy and the Structure of the Software forCondition-Based Maintenance” , INFOR, 39, pp 32-50 ,2001 .

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EXAKT : The CBM Optimizer

Two Keys:

? Risk

? Economics


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Risk factors:• Cholesterol Level• Blood Pressure• Smoking• Lifestyle• Levels of Protein Constituent

Homocysteine

Risk factors:• Oil Analysis (Fe, Cu, Al,

Cr, Pb…..etc.)• Vibration (Velocity and

Acceleration)• Thermography• Visual Inspection

……….………….…………………..

HEART FAILURE

Hazard or Risk = f (Age) + f (Risk factors)

CONDITION BASED MONITORING - AN ANALOGY

EQUIPMENT FAILURE

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Failures/op.hourFailure/flying hour Failures/km.Failures/tonFailures/cycle

ECONOMIC

C f = C+K : Total cost of failure replacement

C p = C : Total cost of preventive replacement

RISK

Mg Al Feet 01183.0867.10518.0483.0

148790148790483.1 ??

??

?

?

??

?

??

? ? ? ? ? ?t n z nt z et t HAZARD ???

??? ???

??

?

?

??

?

?? ...11

1

Contribution of ageto hazard

Contribution of conditioninformation to hazard


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EXAKT V 3.00 Released in December 2001

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EXAKT Procedures Window


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Oil Analysis Data

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Oil Analysis Events Data


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Summary of PHM Parameters

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Summary of PHM Parameters


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Oil Analysis Decision

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Vibration Monitoring Data


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Vibration Analysis Events Data

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TRANSITION PROBABILITY MATRIX ________________________________________

Very Smooth

SmoothRough

Very Rough

Failure

Inspection Interval = 30 days


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Vibration Monitoring Decision

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Warning Limits in ‘ppm’

Normal

< 20

300

>25

Al

Cr

Cu

Fe

Si


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In Operation

Measurements & Decision

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– Twelve covariatesmeasured

– Covariates used: Ironand Sediment – Estimated Saving in

Maintenance Costs:22% for cost ratio 3:1

Oil Analysis data from 50 Wheel Motors

Cardinal River Coals

Source : A.K.S. Jardine et al, “ Optimizing a mine haul truck wheel motors’condition monitoring program" , JQME , 2001 , pp. 286-301.


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Getting the Data• Using ODBC, there are automated ways of

capturing the necessary data from your CMMS andcondition monitoring records.

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MIMOSA Compliance

www.mimosa.org


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What isMIMOSA?

a data model? Called CRIS. (CommonRelational Information Schema)

System BSystem A

Mapping Mapping

CRIS

strctr

M achineryInformationM anagementO penSystemsA lliance

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CBM OPTIMIZATION

Executive Summaries


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©A.K.S. Jardine

Analysis of Shear PumpBearings Vibration Data

– 21 vibration covariatesprovided by accelerometer

Using : – 3 covariates significant

A Check: – Had model been

applied to previous histories – Savings obtained = 35 %

Campbell Soup Company

Source: Jardine, AKS, Joseph, T and Banjevic, D, “ Optimizing condition-based maintenance decisions for equipment subject to vibrationmonitoring” , Journal of Quality in Maintenance Engineering, Vol. 5. No. 3, pp 192-202 , 1999.

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©A.K.S. Jardine

Failed at WorkingAge = 182 days

Inspection atWorkingAge = 175 days

Had we replaced at 175 days…..!!!


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Analysis of Warman-PumpBearings Vibration Data

– Total 8 pumps each with two bearings(16 bearings) analyzed

– 12 vibration covariates identified

Using : – 2 covariates significant – Annual replacement cost savings= 42 %

Feedback: – Model results found realistic by Sasol

plant – Significant vibration covariates identified

by are agreed as a majorproblem

Sasol Plant

Source: P.J. Vlok, J.L. Coetzee. D. Banjevic, A.K.S. Jardine and V.Makis, “An Application of VibrationMonitoring in Proportional Hazards Models for Optimal Component Replacement Decisions”, JORS , Vol. 53,

No. 2, pp. 193-202.

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Open Pit Mining Operation

– Covariates used:Iron, Aluminum,Magnesium

– Saving inMaintenance Costs:25%

– Averagereplacement timeincrease: 13%

– Warranty limit couldbe increased

CAT 793B Transmissions Oil data analyzed


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Definition of Problem• When should this oil

well pumping system(casing, sucker rod, andpump) be replaced,given inspection dataand events data?

• Is it more profitable topreventively replace, orrun until failure?

Oil Well Pumping System

© CBM Lab: University of Toronto

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The EXAKTOptimal

CBM PolicyModel

Types of CBM opt imization models

$100 $30040 h 10 h

MTTR failure

MTTR

planned


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Case Study: Paper Mill• Data used was from a variable speed paper mill

– At present produces carbonless till receipts

• Data was periodically monitored and storedwithin the MIMIC 2001 CMM system – 10 Year history of the plant – Mainly vibration and running speed

• Area called the drier gearbox section• 18 identical units• 8 failure mode parameters• 140 measurements collected over the history

Source: R. Willetts, University of Manchester, England

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Results

• The EXAKT analysis

– Of the 8 original parameters, 5 vertical and 3axially, only 2 parameters were needed

• Acceleration in the vertical direction• Gear mesh in the axial direction


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Hong Kong Mass Transit RailwayCorporation

– Had excessive tractionmotor ball bearingfailures

– CBM to monitor bearinggrease colour

– Changed inspectionsfrom every 3.5 years toannual

– Reduced failures/yr..From 9 to 1

– Reduced total costs by55%

The reality :Failures reduced to 2/yr.

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EXAKT Modeling with MWM Diesel Engine

employed on Halifax Class ships.

Maintenance and Diagnostic Data


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OPG (Pickering Nuclear Station)Hydrodyne seals prevent leakage of heavy water duringfuelling operation: Seal Leak Rate data from 4 reactors

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Integrating EXAKT&

CMMS/EAM/ERP System


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CBMdata

E v e n t

D a t a

C o n d

.

D a t a

Work ordersVA+OA+Othersignals

CMMS

I n s p e c t i o n d a t a E v e n t d a t a

ModelingModule

WMOD WDEC

DecisionModule

DecisionModels

DMDR

D e c i s i o n s

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A Web Agent SiteMining

Manufacturing

Food and Beverage

Pharmaceutical

Power Environmental Pulp and Paper Web Agent Deployment Source: M.Wiseman


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AAAV

Advanced Amphibious Assault Vehicle

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Objectives

• Demonstrate Affordable Prognostic System – Wireless, Small, Scalable, Open

• Provide Robust Fault Detection – Signal Processing, Identification, Isolation, Severity,

Fidelity

• Prognostics for Maintenance Decision Making – Prognosis, Risk, Decision Support


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Prototype System Sensor Placement

-Vibration and Temperature

-Tachometer

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ICHM 20/20

Sensor

Sensor

Sensor

ICHM MCU

DSP

TEDS

Memory

HCI

App. BluetoothRadio

ICHM®

Sensor Module

SignalCond .

A/D

Self-test

Serial

Interface

•120mm x 80mm x 50mm•Supports MIMOSA/OSA CBMInformation Model•Bluetooth™ Wireless Technology


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AAAV

Advanced Amphibious Assault Vehicle

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Model Evolution from PriorKnowledge to Prior Data

The Continuous Improvement Cycle


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Additional CBM References1. Wiseman, M. Optimizing Condition-Based Maintenance, in Maintenance Excellence:Optimizing Equipment Life Cycle Decisions, J.D. Campbell and A.K.S. Jardine(Editors), Marcel Dekker, New York, 2001.2.Jardine, A.K.S., Makis, V., Banjevic D., Bratejevic,D. and Ennis, M., “A DecisionOptimization Model for Condition-Based Maintenance”, Journal of Quality inMaintenance Engineering, Vol. 4, No.2, pp 115-121, 19983. Jardine, AKS, Joseph, T and Banjevic, D, “ Optimizing condition-based maintenancedecisions for equipment subject to vibration monitoring” , Journal of Quality inMaintenance Engineering, Vol. 5. No. 3, pp 192-202, 19995..D Banjevic, Jardine, A.K.S., Makis, V. and M. Ennis, “The Optimal Control LimitPolicy and the Structure of the Software for Condition- Based Maintenance”, INFOR,2001

6. www.mie.utoronto.ca/cbm (For information about the CBM research activities at theUniversity of Toronto)7. www.omdec.com (For information about the EXAKT: CBM Optimization software)

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Summary


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An Overview of EXAKTEvents Data Inspection Data

Replacement Recommendation © CBM Lab: University of Toronto

EXAKT ModelingModule

EXAKT DecisionModule

CBM Model

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Some CBM Optimization Studies


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While much research and productdevelopment in the area ofcondition based maintenancefocuses on data acquisition andsignal processing, the focus of thissession has been the third and finalstep in the CBM process –optimizing the decision making step

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THANK YOU

www.mie.utoronto.ca/cbmwww.omdec.com


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Andrew JardineAndrew Jardine is a professor and principal investigator at the Condition-Based Maintenance laboratory in the Department of Mechanical and IndustrialEngineering at the University of Toronto where the EXAKT software has beendeveloped. He also serves as a Senior Associate Consultant to the GlobalLeader of PricewaterhouseCoopers’ Physical Asset Management practice. Hehas a PhD from the University of Birmingham. He is the author of theAGE/CON and PERDEC software that is licensed to organizations includingtransportat ion, mining, electrical utilities, and process industries. He wrote the

book, “ Maintenance, Replacement and Reliability” first published in 1973. Inaddition to being a sought-after speaker he is a recognized authority in theworld of Reliability Engineering and in the optimization of maintenancedecision making. Professor Jardine was the 1993 Eminent Speaker to theMaintenance Engineering Society of Australia and in 1998 was the first

recipient of the Sergio Guy Memorial Award from the Plant Engineering andMaintenance Association of Canada in recognition of his outstandingcontribution to the Maintenance profession. He is the co-editor with JDCampbell of the 2001 published book Maintenance Excellence: OptimizingEquipment Life Cycle Decisions.

Tel: +1 (416) 978-2921; E.mail: [email protected]

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