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1、Computers&ChemicalEngineeringELSEVIERComputersandChemicalEngineering24(2000)777-784www.elsevier.com/locate/compchemengNeuralnetworkbasedframeworkforfaultdiagnosisinbatchchemicalplantsDiegoRuiza,Jos~MariaNougu~sa,ZulyCalder6nb,AntonioEspufiaa,LuisPuigjanera,,aChemicalEngineeringDepartment
2、,UniversitatPolit~cnicadeCatalunya,Av.Diagonal647,E-08028Barcelona,SpainbUniversidadIndustrialdeSantander,EscueladelnginerladePetrdleos,AA.678,Bucaramanga,ColombiaAbstractInthiswork,anartificialneuralnetwork(ANN)basedframeworkforfaultdiagnosisinbatchchemicalplantsispresented.TheproposedF
3、DSconsistsofanANNstructuresupplementedwithaknowledgebasedexpertsystem(KBES)inablock-orientedconfiguration.ThesystemcombinestheadaptivelearningdiagnosticprocedureoftheANNandthetransparentdeepknowledgerepresentationoftheKBES.TheinformationneededtoimplementtheFDSincludesahistoricaldatabaseo
4、fpastbatches,ahazardandoperability(HAZOP)analysisandamodelofthebatchplant.ThehistoricaldatabasethatincludesinformationrelatedtonormalandabnormaloperatingconditionsisusedtotraintheANNstructure.Thedeviationsoftheon-linemeasurementsfromareferenceprofileareprocessedbyamulti-scalewaveletinord
5、ertodeterminethesingularitiesofthetransientsandtoreducethedimensionalityofthedata.TheprocessedsignalsaretheinputsofanANN.TheANNsoutputsarethesignalsofthedifferentsuspectedfaults.TheHAZOPanalysisisusefultobuildtheprocessdeepknowledgebase(KB)oftheplant.Thisbasereliesontheknowledgeoftheoper
6、atorsandengineersabouttheprocessandallowstheformulationofartificialintelligencealgorithms.Thecasestudycorrespondstoabatchreactor.TheFDSperformanceisdemonstratedthroughthesimulationofdifferentprocessfaults.TheFDSproposedisalsocomparedwithotherapproachesbasedonmulti-wayprincipalcomponentan
7、alysis.?2000ElsevierScienceLtd.Allrightsreserved.Keywords:Faultdiagnosis;Batchplants;Artificialneuralnetworks1.Introductionquencesofabnormalsituationscanbeanticipated.Arobustfaultdiagnosissystem(FDS),thattimelypro-videsthefaultinformationtoboththesupervisoryandSupervision
