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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
October , 2002.
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©A.K.S. Jardine
“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
Number Flight Hours Fe Cr Hazard Rate
1 11770 5 6 0.043
Number Flight Hours Fe Cr Hazard Rate
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
Number Flight Hours Fe Cr Hazard Rate
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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©A.K.S. Jardine
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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©A.K.S. Jardine
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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©A.K.S. Jardine
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
11/8/2002
©A.K.S. Jardine
TRANSITION PROBABILITY MATRIX ________________________________________
Very Smooth
SmoothRough
Very Rough
Failure
Inspection Interval = 30 days
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©A.K.S. Jardine
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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©A.K.S. Jardine
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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©A.K.S. Jardine
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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©A.K.S. Jardine
EXAKT Modeling with MWM Diesel Engine
employed on Halifax Class ships.
Maintenance and Diagnostic Data
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Inspections Table
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Condition-based Optimal Replacement Policy
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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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©A.K.S. Jardine
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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©A.K.S. Jardine
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]