Application of wavelets and neural networks to diagnostic sy

卷积神经网络和一些独立成分分析的外文文献

ComputersandChemicalEngineering23(1999)899–906

Applicationofwaveletsandneuralnetworkstodiagnosticsystem

development,1,featureextraction

B.H.Chen,X.Z.Wang*,S.H.Yang,C.McGreavy

DepartmentofChemicalEngineering,TheUni6ersityofLeeds,LeedsLS29JT,UKReceived14July1997;receivedinrevisedform9March1999;accepted9March1999

Abstract

Anintegratedframeworkforprocessmonitoringanddiagnosisispresentedwhichcombineswaveletsforfeatureextractionfromdynamictransientsignalsandanunsupervisedneuralnetworkforidenti cationofoperationalstates.Multiscalewaveletanalysisisusedtodeterminethesingularitiesoftransientsignalswhichrepresentthefeaturescharacterisingthetransients.Thissimultaneouslyreducesthedimensionalityofthedataandremovesnoisecomponents.Amodi edversionoftheadaptiveresonancetheoryisdeveloped,whichisdesignatedARTnetanduseswaveletfeatureextractionasthesubstituteofthedatapre-processingunit.ARTnetisprovedtobemoreeffectiveindealingwithnoisecontainedinthetransientsignalswhileretainsbeinganunsupervisedandrecursiveclusteringapproach.Theworkisreportedintwoparts.The rstpartisfocusedonfeatureextractionusingwavelets.ThesecondpartdescribesARTnetanditsapplicationtoacasestudyofare nery uidcatalyticcrackingprocess.©1999ElsevierScienceLtd.Allrightsreserved.

1.Introduction

Inmodernprocessplantscontrolledbydistributedcontrolsystems,theroleofoperatorshaschangedfrombeingprimarilyconcernedwithcontroltoabroadersupervisoryresponsibility:analysingoperationaldata,identifyingunusualconditionsastheydevelopandrespondingrapidlyandeffectivelybytakingcorrectiveactions.Thisisachallengingtaskbecauseoftheover-whelmingvolumeofdataoperatorshavetodealwith.Inrecentyearstherehasbeenasigni cantprogressinapplyingintelligentsystemsforprocessmonitoringanddiagnosis.Thisincludestheuseofneuralnetworks,multivariatestatisticalanalysis,expertsystemsaswellasqualitativesimulation.Itisrecognisedthatinprocessmonitoringanddiagnosis,puterbasedprocessingofdynamictrendsignalsisaimedatnoiseremovaland

*Correspondingauthor.Tel.:+44-113-233-2427;fax:+44-113-233-2405.

E-mailaddress:x.z.wang@leeds.ac.uk(X.Z.Wang)

dimensionreductionusingminimumdatapointstocapturethefeaturescharacterisingthetrendsignals.Variousapproacheshavebeenproposedandtheiref-fectivenessdependslargelyonhowtheprocessedinfor-mationistobeused,i.e.byhumanexperts,expertsystemsorneuralnetworks.Inthiswork,anintegratedframework,ARTnetisdevelopedandsubsequentlyap-pliedtoacasestudyofare nery uidcatalyticcrack-ingprocess.ARTnetisamodi edversionoftheadaptiveresonancetheory(ART2)(CarpenterandGrossberg,1987;Whiteley&Davis,1992,1994;White-ley,Davis,Mehrotra,&Ahalt,1996)whichuseswavelettransformsasthesubstituteofthedatapre-processingunitofART2.

Theworkisreportedintwoparts.The rstpartisfocusedonfeatureextractionfromdynamictransientsignalsusingwavelettransformsandthesecondpartisconcernedwiththeintroductionofARTnetanditsapplicationtoacasestudyofare nery uidcatalyticcrackingprocess.The rstpartisorganisedasfollows.InSection2somerepresentativeapproachesforfeatureextractionarebrie yreviewed.Thisnaturallyleadstotheintroductionofwaveletmultiscaleanalysisforfea-tureextractioninSection3.Waveletmultiscaleanalysis ndstheextremaofatransientsignalandanimportant

0098-1354/99/$-seefrontmatter©1999ElsevierScienceLtd.Allrightsreserved.PII:S0098-1354(99)00258-6