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428 404
429 405
430 406
431 407
432 408
433 409
434 410
435 411
436 412
437 413
438 414
439 415
440 416
441 417
442 418
443 419
Contributors
Elias Strangas
Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan, USA
Guy Clerc
Univ Lyon, University Claude Bernard Lyon 1, Ecole Centrale de Lyon, INSA Lyon, Ampère Laboratory, CNRS UMR5005, F-69622, Villeurbanne, France
Hubert Razik
Univ Lyon, University Claude Bernard Lyon 1, Ecole Centrale de Lyon, INSA Lyon, Ampère Laboratory, CNRS UMR5005, F-69622, Villeurbanne, France
Abdenour Soualhi
Univ Lyon, University of Jean Monnet, Laboratory LASPI EA-3059, F-42100 Saint Etienne, France
Acknowledgments
The authors would like to thank their doctoral students and collegues for their contributions to research in the area of failure diagnosis and prognosis in the field of the electrical engineering and their contribution to the writing of this book:
Anmol AGARWAL
Andrew BABEL
Mohamed BEN MARZOUG
Romain BREUNEVAL
Jorge CINTRON-RIVERA
Alexandre EID
Samuel EKE
Nathan EVANS
Shanelle FOSTER
Reemon HADDAD
Malorie HOLOGNE
Abdesselam LEBAROUD
John NEELY
Rodney SINGLETON
Diego VELAZCO
Sajjad ZAIDI
Wesley ZANARDELLI
Yang ZETONG
Shaopo HUANG
Selin AVIYENTE
A. E. G. H.
Acronyms
α, βDirect and quadrature axis of a fixed frameαFiring delay angleβWeibull parameterλFailure rateλFlux linkagesωSpeed of the rotor for two-pole equivalent machine, electrical rotor speedωmMechanical rotor speed, ωs = ω/pωrAngular speed of rotor variables, for an equivalent 2-pole machineωsSpeed of the stator variables for an equivalent two-pole machineΦFlux in WbΦTransition matrixBRRemanenceHcCoercivityL1The number of revolutions or hours that 90% of a group of apparently identical components will complete or exceed before failureLRRotor inductanceLSStator inductancepNumber of pole pairssSlip, s = (ωs – ω)/ωsTTorque in NmACAlternative currentANNArtificial Neural NetworkBNBaysian NetworkCBMCondition-Based MaintenanceCDFCumulative Distribution Functiond, q, 0Direct, quadrature and homopolar axis of a synchronous reference frameDAGDirected Acyclic GraphDCDirect currentDFDissipation factorEKFExtended Kalman FilterEMFElectromotive ForceFEAFinite element analysisFFTFast Fourier TransformFSFault signatureFTFault treeFTAFault tree analysisFTCFault-tolerant controlGaNGallium ArsenideHHTHilbert-Huang TransformHSCTHigh-sensitivity current transformerHSMMHidden Semi-Markov ModelIGBTInsulated-gate bipolar transistorISIInsulation health indicatorITSCInter-turn short circuitMBFFuzzy Membership FunctionsMCMarkov ChainMCMonte CarloMFCMetallized film capacitorMMFMagnetomotive force, ℱMOSFETMetal–oxide–semiconductor field-effect transistorMRASModel Reference Adaptive SystemMTTFMean time to failureMTTRMean time to repairNNNeural networkPFParticle filterPIPolarization indexPMACPermanent magnet ACPWMPulse width modulationRReliability functionRACReliability associated costsRBDReliability block diagramRFCRotor field-oriented controlRMSDRoot-mean-square deviationRNNRecurrent neural networkRULRemaining useful lifeSiSiliconSiCSilicon carbideSTFTShort-time Fourier transformSVMSupport vector machineTFTime-FrequencyWTWavelet transform
Introduction
The progress in electrification of manufacturing processes, transportation, commercial, and residential applications is accelerating exponentially. This movement is supported by an increasing acceptance and use of electrical drives, which have progressed in terms of cost, size, efficiency, and performance. This progress enabled the use of drives in current and new applications that benefit from these characteristics. This resulted in lower environmental pollution, and applications requiring higher flexibility, such as electric and hybrid vehicles, more electric airplanes and electric ships, new energy sources, industrial controls, consumer electronics,