建筑电气故障诊断文献综述及外文文献资料.docx
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建筑电气故障诊断文献综述及外文文献资料
本份文档包含:
关于该选题的外文文献、文献综述
一、外文文献
文献信息
标题:
ReviewstotheResearchonBuildingElectricalIntelligentFaultSelf-diagnosis
作者:
MaileT;VincentiM
期刊名称:
BuildingandEnvironment;第8卷;第3期;页码:
29-42
年份:
2015
ReviewstotheResearchonBuildingElectricalIntelligentFaultSelf-diagnosis
Abstract
Wesummarizesomemethodsofthefaultdiagnosisinthepaper,basedontheFaultSelf-diagnosisresearching.BecauseoftheBuildingElectricalFaultSelf-diagnosisSystemisnotresearchedindepth,ourgroupfoundtheproblemofFaultSelf-diagnosisandproposetakingtheartificialintelligencemethod.Especiallyinthisfewyears,wediscussedtheapplyingofmanykindsofextensionsinBuildingElectricalFaultSelf-diagnosisSystembasedontheneuralnetworkandtheresultofdiscussingcanprovidesomenewideasforfurtherresearchingofFaultSelf-diagnosis.
Keywords:
Faultself-diagnosis Buildingelectrical Detectiontechnology
1 Introduction
Theelectricalandelectronicequipmentinbuildingsbringstheconveniencetopeople’slifeandwork,andhasalsoimprovedthequalityoflife.However,itwillbeahugedisasteriftheelectricalandelectronicequipmentbreakdown.Inordertopreventtheaccidentinbuildingofelectricalequipment,First,theequipment’sproduction,installation,operationconformtothecorrespondingdesignspecificationandprocess,itcanfundamentallyenhanceequipmentreliabilityanddecreasethefailurerate.Second,aneffectivemanagementmeasureincludestheequipment’smaintenanceandcarewhentheabnormalsituationcanbefoundearlier,foravoidingthispreventingaccidentsbeforetheyoccur.
Early,thewayofdetectingthebuildingelectricalequipmentfaultisthroughthemaintenanceworkerstocheckup.Therearealotofsubjectivityanduncertaintyinthisway.Itcannotsatisfytherequirementsthroughthefactitiousempiricalmaintenancealongwiththeelectricalequipmentwerebecomemoreandmorecomplicatedandtheirkindsarebecomingmoreandmore.Self-diagnosissystembecomesrealisticdemand.Atpresent,thecountriesinallovertheworldpayalotofmoneyforequipment’smaintenanceeveryyearbecauseofthemaintenancesystemsthatbeusedontheconstructionofelectricalequipmenthaveseriousdefects,suchasthefrequenttemporaryrepair,inadequatemaintenanceorsuperfluousmaintenanceandblindmaintenance,etc.Itisanimportantproblemthathowtosavemaintenance’scosttoconstructionofelectricalequipmentandatthesametimeensurethatthesystemhashighreliabilityforthesystem’soperationpersonnel.Thefault’smaintenanceresearchbasedontheequipment’sstatemonitoringandadvancedfaultdiagnosistechnologydevelopingbecameanimportantresearchintheconstructionofelectricalsystem,becauseofthesensingtechnology,microelectronics,computerhardwareandsoftware,digitalsignalprocessingtechnology,artificialneuralnetwork,expertsystem,fuzzysettheoryandothercomprehensiveintelligentsystemwareappliedtoconditionmonitoringandfaultdiagnosis.Specifictotheconstructionofelectricalfield,therearefivekindsofdirectbenefitstocarryouttheself-diagnosisoffaultinconstructionofelectricalequipmentsystem:
1.savealotofelectricalequipment’smaintenancefee;2.increasetheavailablecoefficientofelectricenergy;3.extendtheservicelifeofelectricalequipment;4.ensurereliabilityofthepowersupplyforelectricalequipment;5.reducethemaintenancecoastofelectricalequipmentfaultandthemaintenancerisk.Thispapermainlyintroducessystemevolution,thegeneralsituationandstateofdevelopmentandtheproblemofself-diagnosismaintenance.
Thisprojectadoptsmethodsthatarebasedonthemethodofclassification,andconsideredaboutpeopleneedtohaveanaccuratefaultclassificationandfaultlocationintheconstructionofelectricalfailure.Neuralnetwork,akindofclassificationmethod,hasthegoodpatternrecognitionabilityandneuralnetworkisgoodatdealingwiththeproblemthathassomestrongcouplingnonlinearcorrelationaboutmappingofsymptomsetorfaultset.Then,wedecidedtohaveafurtherstudyaboutthetheoryandpracticebasedonthefaultdiagnosisofneuralnetwork.
2 TheMainTypesandProgressoftheApplicationofFaultDiagnosisTheory
Thedevelopmentofthetheoryoffaultdiagnosishascometoanewstagenow.Thecoreisthatthemodernknowledgesystemofvariousdisciplineswassynthesized.Thefaultdiagnosistheoryaboutcontroltheoryandcontrolengineeringinvolvedthemoderncontroltheory,signalandsystem,powerelectronictechnology,modernstatistics,systemidentificationandpatternrecognitionandotherkindsofdiscipline.Inrecentyears,withtheresearchscopeoffaultdiagnosiscontinuestoexpand,itformedthatsomemainstreammethodsoffaultdiagnosis.
2.1 TheApplicationofArtificialIntelligenceintheElectricalSystem’sFaultSelf-diagnosis
TheArtificialIntelligence(AI)systemwasdevelopedandappliedinthefaultself-diagnosisofelectricalequipmentfield.ThereforetheExpertSystemandArtificialNeuralNetworkarethemostconspicuous.Theelectricalequipmentfaultself-diagnosisexpertsystemthathasbeendevelopedhastheknowledgebase,database,reasoningmachine,interpretationsystemandman-machineinterfacefiveparts.Theelectricalequipmentfaultself-diagnosisexpertsystem’sknowledgebaseusesthemodularstructure.AstheFigshows:
Theartificialneuralnetworkalsousesthemodularstructure.ItusestheRBFradialbasisfunctionneuralnetwork,bringinthefuzzylogictheoryandjudgesomefaultinelectricalequipmenttopreventtheoccurrenceoffailure.Theelectricalequipmentsystemcanmakeearlyanticipationforinternallatentfailureofoperationalelectricalequipmentanditcanprovide“consultation”totheoperatingpersonnelwhooperatetheelectricalequipment.Electricalequipmentfaultself-diagnosissystemknowledgebasemodulechart.
Atpresent,thefaultself-diagnosissystemofelectricalequipmentusingtheproducetypemodelsystemthatisthemostcommon,andtheknowledgebaseusethemodularstructure.Theknowledgebasemoduleismutuallyindependentanditisadvantageousinthemodification,extensionandrenewalofknowledgebase.Ittakesagreatconveniencetotheknowledgebase’smaintenanceofinthefaultself-diagnosissystemofelectricalequipment.
Usingthecharacteristicsoftheexpertsystemprogramminglanguagetorealizethereverseinferenceofelectricalequipmentfaultself-diagnosissystem’sobjectandbringinthefuzzylogicreasoningmodelandsuccessfullydealingwiththeproblemofsomefuzziness.
Electricalequipmentfaultself-diagnosissystemdatabaseconsistsofelectricalparameteranalysisanddynamicdatabase.
2.2 TheMethodBasedontheTheoryModelofElectricalTestingEstimate’sFaultSelf-diagnosis
Aseveryoneknows,peopleusuallyusethestateequationofsinglecontinuousvariationtoaccuratelydescribewhentheelectricalequipmentsystemsendsfailure.Therandomhybridsystemmodelismoreaccuratemathematicalmodelinthefaultself-diagnosissystemofelectricalequipment.Thisrandomhybridsystemmodelisdifferentwithstochasticsystem,becausethestateofrandomhybridsystemcanbediscreteandcontinuous,sowecanknowthattherandomhybridsystemhascharacteristicofrandom.Thepassivefailuremodecontrolsystemthatisakindofelectricaltestsystemisakindoftypicalrandomhybridsystemmodelandtheyaredifferentbetweenthefailuremode’sstructuresoftheinternalelectricaltestsystem,andwecanknowthataredifferentbetweenthefailuremode’sstructureandnormalmode’sstructure.
Intheelectricaltestsystem,themultiplemodelestimationalgorithmincludetherandomhybridsystemmodel’sdesign,filter’sselection,thereinitializationofestimationfusionandfilter.Theprincipleblockdiagramofelectricaltestsystem’srandomhybridsystemmodelestimationalgorithmisshowninFig. 42.2.
MultiplemodelestimationalgorithmisbroughtoutbyMaiglwhenheresearchedtheoptimizationself-adaptionestimationofrandomsampledataprocessing.Butintheearly,multiplemodelalgorithmdidnotconsideraboutthemodelsswitchinganditbelongthemultiplemodelalgorithmwithstaticzeroinput.Inthefewyears,ChinaandothercountriesbegantoresearchtheMultipleModelSelf-adaptiveEstimateandMultipleModelSelf-adaptiveControl,theyareakindofmultiplemodelapproachoftypicalzeroinputinteraction.ThemethodofMultipleModelSelf-adaptiveEstimationmaybegeneratetheelectricalequipmentsystem’sestimationperformancedeteriorationandevenmaketheelectricalequipmentsystem’sestimatorappearthedivergence,becauseofthemethodofMultipleModelSelf-adaptiveEstimationlackthenecessaryinputmutualinformation.
2.3 TheElectricalSystem’sFaultSelf-diagnosisBasedonFuzzyNeuralNetwork
Whentheelectricalequipmentsystemisdoingthefaultself-diagnosis,thefuzzyneuralnetworkcanextractthecurrentelectricalequipmentsystem’sfuzzymodel’sdescriptionwithnonlinearityandcomparewitheachreferencemodel,thuspeoplecangettheaccuratefaultdiagnosis.ThesystemmodeloffuzzyneuralnetworkastheFig. 42.3 shows.Themethodofelectricalequipmentsystem’sfaultself-diagnosisbyusingneuralnetwork,althoughwecangetthedataoffailurecharacteristicsthroughthenetworkself-learningbutitistardinesstocomparewithsomecomplexelectricalequipmentsystemlearning.Andtheneuralnetworkisnotfullyinthenetwork’sstructure,soitisdifficulttoextracttheperformanceofneuralnetworkthroughtoanalysisthedataoftrainingcharacteristics.Inaddition,m