Prognostics and health management : a practical approach to improving system reliability using conditioned-based data

385 Pages·2019·23.81 MB·English

by  Goodman, Douglas, Hofmeister, James P., Szidarovszky, Ferenc

PrognosticsandHealthManagement
WileySeriesinQuality&ReliabilityEngineering
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Prognostics and Health Management
APracticalApproachtoImprovingSystemReliabilityUsing
Condition-BasedData
DouglasGoodman,JamesP.HofmeisterandFerencSzidarovszky
RidgetopGroup,Inc.,Arizona,USA
Thiseditionfirstpublished2019
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LibraryofCongressCataloging-in-PublicationData
Names:Goodman,Douglas(Industrialengineer),author.|Hofmeister,James
P.,author.|Szidarovszky,Ferenc,author.
Title:Prognosticsandhealthmanagement:apracticalapproachtoimproving
systemreliabilityusingcondition-baseddata/DouglasGoodman,Chief
Engineer,RidgetopGroup,Inc.,JamesP.Hofmeister,Distinguished
Engineer,RidgetopGroup,Inc.,FerencSzidarovszky,Senior
Researcher,RidgetopGroup,Inc.
Description:Hoboken,NJ,USA:Wiley,[2019]|Includesbibliographical
referencesandindex.|
Identifiers:LCCN2018060348(print)|LCCN2019000608(ebook)|ISBN
9781119356691(AdobePDF)|ISBN9781119356707(ePub)|ISBN9781119356653
(hardcover)
Subjects:LCSH:Machinery–Reliability.|Equipmenthealthmonitoring.|
Machinery–Maintenanceandrepair–Planning.|Structural
failures–Mathematicalmodels.
Classification:LCCTJ174(ebook)|LCCTJ174.G662019(print)|DDC
621.8/16–dc23
LCrecordavailableathttps://lccn.loc.gov/2018060348
CoverDesign:Wiley
CoverImage:©SergeyNivens/Shutterstock
Setin10/12ptWarnockProbySPiGlobal,Chennai,India
PrintedinGreatBritainbyTJInternationalLtd,Padstow,Cornwall
10 9 8 7 6 5 4 3 2 1
v
Contents
ListofFigures xi
SeriesEditor’sForeword xxi
Preface xxiii
Acknowledgments xxvii
1 IntroductiontoPrognostics 1
1.1 WhatIsPrognostics? 1
1.1.1 ChapterObjectives 1
1.1.2 ChapterOrganization 3
1.2 FoundationofReliabilityTheory 3
1.2.1 Time-to-FailureDistributions 4
1.2.2 ProbabilityandReliability 6
1.2.3 ProbabilityDensityFunction 7
1.2.4 RelationshipsofDistributions 10
1.2.5 FailureRate 10
1.2.6 ExpectedValueandVariance 16
1.3 FailureDistributionsUnderExtremeStressLevels 18
1.3.1 BasicModels 18
1.3.2 CumulativeDamageModels 21
1.3.3 GeneralExponentialModels 21
1.4 UncertaintyMeasuresinParameterEstimation 23
1.5 ExpectedNumberofFailures 26
1.5.1 MinimalRepair 26
1.5.2 FailureReplacement 28
1.5.3 DecreasedNumberofFailuresDuetoPartialRepairs 30
1.5.4 DecreasedAgeDuetoPartialRepairs 30
1.6 SystemReliabilityandPrognosisandHealthManagement 31
1.6.1 GeneralFrameworkforaCBM-BasedPHMSystem 32
1.6.2 RelationshipofPHMtoSystemReliability 34
1.6.3 DegradationProgressionSignature(DPS)andPrognostics 35
1.6.4 IdealFunctionalFailureSignature(FFS)andPrognostics 37
1.6.5 Non-idealFFSandPrognostics 41
1.7 PrognosticInformation 41
1.7.1 Non-ideality:Initial-EstimateErrorandRemainingUsefulLife(RUL) 42
1.7.2 ConvergenceofRULEstimatesGivenanInitialEstimateError 44
vi Contents
1.7.3 PrognosticDistance(PD)andConvergence 45
1.7.4 Convergence:FigureofMerit(𝜒 ) 45
𝛼
1.7.5 OtherSourcesofNon-idealityinFFSData 46
1.8 DecisionsonCostandBenefits 47
1.8.1 ProductSelection 47
1.8.2 OptimalMaintenanceScheduling 49
1.8.3 Condition-BasedMaintenanceorReplacement 54
1.8.4 PreventiveReplacementScheduling 55
1.8.5 ModelVariantsandExtensions 58
1.9 IntroductiontoPHM:Summary 60
References 60
FurtherReading 62
2 ApproachesforPrognosisandHealthManagement/Monitoring
(PHM) 63
2.1 IntroductiontoApproachesforPrognosisandHealth
Management/Monitoring(PHM) 63
2.1.1 Model-BasedPrognosticApproaches 63
2.1.2 Data-DrivenPrognosticApproaches 63
2.1.3 HybridPrognosticApproaches 64
2.1.4 ChapterObjectives 64
2.1.5 ChapterOrganization 64
2.2 Model-BasedPrognostics 65
2.2.1 AnalyticalModeling 66
2.2.2 DistributionModeling 71
2.2.3 PhysicsofFailure(PoF)andReliabilityModeling 72
2.2.4 AccelerationFactor(AF) 74
2.2.5 ComplexityRelatedtoReliabilityModeling 76
2.2.6 FailureDistribution 78
2.2.7 MultipleModesofFailure:FailureRateandFIT 79
2.2.8 AdvantagesandDisadvantagesofModel-BasedPrognostics 79
2.3 Data-DrivenPrognostics 80
2.3.1 StatisticalMethods 80
2.3.2 MachineLearning(ML):ClassificationandClustering 85
2.4 Hybrid-DrivenPrognostics 90
2.5 AnApproachtoCondition-BasedMaintenance(CBM) 92
2.5.1 ModelingofCondition-BasedData(CBD)Signatures 92
2.5.2 ComparisonofMethodologies:LifeConsumptionandCBDSignature 92
2.5.3 CBD-SignatureModeling:AnIllustration 93
2.6 ApproachestoPHM:Summary 103
References 103
FurtherReading 106
3 FailureProgressionSignatures 107
3.1 IntroductiontoFailureSignatures 107
3.1.1 ChapterObjectives 107
3.1.2 ChapterOrganization 108
Contents vii
3.2 BasicTypesofSignatures 108
3.2.1 CBDSignature 109
3.2.2 FFPSignature 114
3.2.3 TransformingFFPintoFFS 118
3.2.4 TransformingFFPintoaDegradationProgressionSignature(DPS) 120
3.2.5 TransformingDPSintoDPS-BasedFFS 122
3.3 ModelVerification 124
3.3.1 SignatureClassification 124
3.3.2 VerifyingCBDModeling 125
3.3.3 VerifyingFFPModeling 127
3.3.4 VerifyingDPSModeling 128
3.3.5 VerifyingDPS-BasedFFSModeling 129
3.4 EvaluationofFFSCurves:Nonlinearity 130
3.4.1 SensingSystem 132
3.4.2 FFSNonlinearity 132
3.5 SummaryofDataTransforms 134
3.6 DegradationRate 140
3.6.1 ConstantDegradationRate:LinearDPS-BasedFFS 140
3.6.2 NonlinearDegradationRate 141
3.7 FailureProgressionSignaturesandSystemNodes 142
3.8 FailureProgressionSignatures:Summary 144
References 145
FurtherReading 146
4 Heuristic-BasedApproachtoModelingCBDSignatures 147
4.1 IntroductiontoHeuristic-BasedModelingofSignatures 147
4.1.1 ReviewofChapter3 147
4.1.2 Theory:HeuristicModelingofCBDSignatures 149
4.1.3 ChapterObjectives 150
4.1.4 ChapterOrganization 151
4.2 GeneralModelingConsiderations:CBDSignatures 151
4.2.1 NoiseMargin 152
4.2.2 DefinitionofaDegradation-SignatureModel 152
4.2.3 FeatureData:NominalValue 152
4.2.4 FeatureData,Fault-to-FailureProgressionSignature,and
Degradation-SignatureModel 153
4.2.5 ApproachtoTransformingCBDSignaturesintoFFSData 153
4.3 CBDModeling:Degradation-SignatureModels 154
4.3.1 RepresentativeExamples:Degradation-SignatureModels 155
4.3.2 ExamplePlotsofRepresentativeFFPDegradationSignatures 167
4.3.3 ConvertingDecreasingSignaturestoIncreasingSignatures 167
4.4 DPSModeling:FFPtoDPSTransformModels 168
4.4.1 DevelopingTransformModels:FFPtoDPS 168
4.4.2 ExamplePlotsofFFPSignaturesandDPSSignatures 177
4.5 FFSModeling:FailureLevelandSignatureModeling 177
4.5.1 DevelopingDPS-BasedFailureLevel(FL)ModelsUsingFFPDefinedFailure
Levels 177
viii Contents
4.5.2 ModelingResultsforFailureLevels:FFP-BasedandDPS-Based 182
4.5.3 TransformingDPSDataintoFFSData 183
4.6 Heuristic-BasedApproachtoModelingofSignatures:Summary 183
References 186
FurtherReading 187
5 Non-IdealData:EffectsandConditioning 189
5.1 IntroductiontoNon-IdealData:EffectsandConditioning 189
5.1.1 ReviewofChapter4 189
5.1.2 DataAcquisition,Manipulation,andTransformation 189
5.1.3 ChapterObjectives 191
5.1.4 ChapterOrganization 194
5.2 Heuristic-BasedApproachAppliedtoNon-IdealCBDSignatures 194
5.2.1 SummaryofaHeuristic-BasedApproachAppliedtoNon-IdealCBD
Signatures 195
5.2.2 ExampleTargetforPrognosticEnabling 196
5.2.3 NoiseisanIssueinAchievingHighAccuracyinPrognosticInformation 200
5.3 ErrorsandNon-IdealityinFFSData 202
5.3.1 NoiseMarginandOffsetErrors 202
5.3.2 MeasurementError,Uncertainty,andSampling 203
5.3.3 OtherSourcesofNoise 214
5.3.4 DataSmoothingandNon-IdealityinFFSData 218
5.4 HeuristicMethodforAdjustingFFSData 223
5.4.1 DescriptionofaMethodforAdjustingFFSData 223
5.4.2 AdjustedFFSData 224
5.4.3 DataConditioning:AnotherExampleDataSet 225
5.5 Summary:Non-IdealData,Effects,andConditioning 227
References 229
FurtherReading 230
6 Design:RobustPrototypeofanExemplaryPHMSystem 233
6.1 PHMSystem:Review 233
6.1.1 Chapter1:IntroductiontoPrognostics 233
6.1.2 Chapter2:PrognosticApproachesforPrognosisandHealth
Management 234
6.1.3 Chapter3:FailureProgressionSignatures 237
6.1.4 Chapter4:Heuristic-BasedApproachtoModelingCBDSignatures 239
6.1.5 Chapter5:Non-IdealData:EffectsandConditioning 239
6.1.6 ChapterObjectives 243
6.1.7 ChapterOrganization 245
6.2 DesignApproachesforaPHMSystem 246
6.2.1 SelectingandEvaluatingTargetsandTheirFailureModes 247
6.2.2 OfflinePrognosticApproaches:SelectingResults 248
6.2.3 SelectingaBaseArchitecturefortheOnlinePhase 248
6.3 SamplingandPolling 249
6.3.1 Continual–PeriodicSampling 249
6.3.2 Periodic-BurstSampling 250