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基于復(fù)合神經(jīng)網(wǎng)絡(luò)的排水系統(tǒng)故障診斷研究摘要:
排水系統(tǒng)是城市基礎(chǔ)設(shè)施中重要的組成部分,其在城市環(huán)境中具有重要的作用,能有效地提高城市環(huán)境的質(zhì)量。但是,排水系統(tǒng)在長期運(yùn)行過程中會出現(xiàn)各種各樣的故障,影響其正常運(yùn)行。針對這一問題,本文提出了一種基于復(fù)合神經(jīng)網(wǎng)絡(luò)的排水系統(tǒng)故障診斷方法。
首先,通過對排水系統(tǒng)進(jìn)行實(shí)時(shí)監(jiān)測,獲取系統(tǒng)運(yùn)行數(shù)據(jù),并預(yù)處理數(shù)據(jù)以提高數(shù)據(jù)質(zhì)量。然后,采用復(fù)合神經(jīng)網(wǎng)絡(luò)對數(shù)據(jù)進(jìn)行訓(xùn)練,以建立故障預(yù)測模型。該模型采用多層神經(jīng)元結(jié)構(gòu),能夠更加準(zhǔn)確地預(yù)測排水系統(tǒng)未來的故障情況,并給出故障預(yù)警提示。最后,通過實(shí)驗(yàn)驗(yàn)證,證明該方法能夠有效地提高排水系統(tǒng)的運(yùn)行效率和穩(wěn)定性。
本文的研究結(jié)果為排水系統(tǒng)的故障預(yù)防和修復(fù)提供了可靠的技術(shù)支持,具有很大的應(yīng)用價(jià)值。
關(guān)鍵詞:排水系統(tǒng);故障診斷;復(fù)合神經(jīng)網(wǎng)絡(luò);預(yù)測模型;實(shí)時(shí)監(jiān)測
Abstract:
Thedrainagesystemisanimportantpartofurbaninfrastructure,whichplaysanimportantroleinimprovingthequalityofurbanenvironment.However,variousfaultswilloccurinthedrainagesystemduringitslong-termoperation,whichwillaffectitsnormaloperation.Inordertosolvethisproblem,thispaperproposesafaultdiagnosismethodfordrainagesystembasedoncompositeneuralnetwork.
Firstly,thedrainagesystemismonitoredinrealtimetoobtainsystemoperatingdata,andthedataispreprocessedtoimprovedataquality.Then,thecompositeneuralnetworkisusedtotrainthedatatoestablishafaultpredictionmodel.Themodeladoptsamulti-layercellstructure,whichcanpredictthefuturefaultsofthedrainagesystemmoreaccuratelyandgivefaultwarningtips.Finally,throughexperimentalverification,itisprovedthatthismethodcaneffectivelyimprovetheoperationefficiencyandstabilityofthedrainagesystem.
Theresearchresultsofthispaperprovidereliabletechnicalsupportforfaultpreventionandrepairofdrainagesystem,andhavegreatapplicationvalue.
Keywords:drainagesystem;faultdiagnosis;compositeneuralnetwork;predictionmodel;real-timemonitorinIntroduction
Thedrainagesystemplaysacriticalroleinurbaninfrastructure,whichisresponsiblefordrainingawaystormwaterandwastewater.Awell-functioningdrainagesystemisessentialtopreventfloodingandwaterpollution.However,duetovariousreasonssuchasaging,blockage,anddamage,thedrainagesystemmayfailtoperformadequately.Therefore,itisnecessarytodevelopeffectivemethodsforfaultdiagnosisandpredictiontoensurethestableoperationofthedrainagesystem.
Inrecentyears,manystudieshavefocusedonthefaultdiagnosisofthedrainagesystem.Someresearchershaveproposeddata-drivenmethods,suchasartificialneuralnetworks(ANNs)andsupportvectormachines(SVMs),topredictthefaultstatusofthedrainagesystem.However,thesemethodshavelimitationsintermsofaccuracyandefficiency.Toaddressthisissue,thispaperproposesafaultdiagnosismethodbasedonacompositeneuralnetworkandreal-timemonitoring.
Methodology
Theproposedfaultdiagnosismethodconsistsofthreestages:datapreprocessing,modeltraining,andreal-timemonitoring.Inthedatapreprocessingstage,therawdatafromthedrainagesystemarepreprocessedtoeliminatenoiseandoutliers.Then,theprocesseddataareusedtotrainacompositeneuralnetworkthatcombinestheadvantagesofconvolutionalneuralnetworks(CNNs)andlong-shorttermmemorynetworks(LSTMs).Thecompositeneuralnetworkcaneffectivelycapturethespatiotemporalfeaturesofthedrainagesystemandachievehighaccuracyinfaultdiagnosis.
Inthereal-timemonitoringstage,thetrainedpredictionmodelisdeployedtothedrainagesystemtocontinuouslymonitorthesystem'sperformance.Whenthesystem'sperformancedeviatesfromthenormalstate,thepredictionmodelwillgivefaultwarningtipstotheoperators,indicatingthepossiblecausesandlocationsofthefault.Theoperatorscantakeappropriatemeasurestopreventtheoccurrenceofthefaultorrepairthesystemtimely.
ExperimentalResults
Toevaluatetheeffectivenessoftheproposedmethod,experimentswereconductedonarealdrainagesysteminacityinChina.TheexperimentalresultsshowthatthecompositeneuralnetworkcanachievehigheraccuracythanthetraditionalANNandSVMmethodsinfaultprediction.Moreover,thereal-timemonitoringsystemcaneffectivelyimprovetheoperationefficiencyandstabilityofthedrainagesystem,reducingthefrequencyofsystemfailuresandmaintenancecosts.
Conclusion
Thispaperproposesafaultdiagnosismethodbasedonacompositeneuralnetworkandreal-timemonitoringforthedrainagesystem.Theproposedmethodcanaccuratelypredictthefaultstatusofthedrainagesystemandprovidetimelywarningtipstotheoperators,ensuringthestableoperationofthesystem.Theexperimentalresultsdemonstratetheeffectivenessoftheproposedmethod,whichhasgreatapplicationvalueinthefaultpreventionandrepairofthedrainagesystemInsummary,thefaultdiagnosismethodbasedonacompositeneuralnetworkandreal-timemonitoringforthedrainagesystemisareliableandefficientapproachformaintainingthesmoothoperationofthesystem.Theproposedmethodcombinestheadvantagesofdifferenttypesofneuralnetworksandthereal-timemonitoringsystemtoaccuratelyidentifyfaultsandprovidereliablewarningtipstotheoperators.
Comparedtootherexistingfaultdiagnosismethods,theproposedmethodhasseveraladvantages.Firstly,itcanidentifydifferenttypesoffaultsaccurately,includingpartialblockages,completeblockages,andleakage,whichiscrucialformaintainingthedrainagesystem'ssmoothoperation.Secondly,themethodcanprovidetimelywarningstooperators,whichisessentialtopreventfurtherdamageandavoidcostlyrepairs.Thirdly,theproposedmethodiscomputationallyefficient,makingiteasiertoimplementandruninreal-time.
Overall,theproposedmethodoffersareliableandefficientwaytopreventandrepairfaultsinthedrainagesystem.Futureresearchcouldfocusonapplyingtheproposedmethodtodifferenttypesofdrainagesystemsandinvestigatingtheeffectivenessofthemethodinreal-timeoperations.Additionally,exploringwaystoimprovetheaccuracyandefficiencyofthemethodcouldleadtofurtherimprovementsandapplicationoftheproposedmethodologyOneareaoffutureresearchcouldbeexploringthepotentialuseofmachinelearningalgorithmsinconjunctionwiththeproposedmethodtoimprovetheaccuracyofdetectingandpredictingfaultsinthedrainagesystem.Machinelearningalgorithmscouldbetrainedonlargeamountsofhistoricdatafromthedrainagesystemtoidentifypatternsandtrendsthatmaynotbeimmediatelyapparenttohumanoperators.Thiscouldpotentiallyleadtomoreproactivemaintenanceandrepairstrategies.
Anotherpotentialareaofresearchcouldbeinvestigatingtheeffectivenessoftheproposedmethodinlarger,morecomplexdrainagesystems.Whiletheexperimentsconductedinthisstudywereconductedonasmall-scalesystem,themethodologymaynotnecessarilytranslatetolargersystemswithmorecomplexgeometriesandflowpatterns.Therefore,furtherresearchisneededtodeterminehowtheproposedmethodcouldbeadaptedandoptimizedforlargersystems.
Finally,itmaybebeneficialtoexplorehowtheproposedmethodcouldbeintegratedintoexistingdrainagesystemsandmanagementframeworks.Forexample,couldthemethodbeintegratedwithexistingsupervisorycontrolanddataacquisition(SCADA)systemsthatarecommonlyusedtomonitorandcontrolwaterdistributionsystems?Additionally,howwouldtheproposedmethodfitintoexistingmaintenanceschedulesandoperations?Answeringthesequestionscouldprovidevaluableinsightsintothepracticalityandfeasibilityofimplementingtheproposedmethodinreal-worldscenarios.
Inconclusion,theproposedfault
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