基于復(fù)合神經(jīng)網(wǎng)絡(luò)的排水系統(tǒng)故障診斷研究

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(huì)出現(xiàn)各種各樣的故障，影響其正常運(yùn)行。針對(duì)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(duì)排水系統(tǒng)進(jìn)行實(shí)時(shí)監(jiān)測(cè)，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(duì)數(shù)據(jù)進(jìn)行訓(xùn)練，以建立故障預(yù)測(cè)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(cè)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(cè)模型；實(shí)時(shí)監(jiān)測(cè)

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