基于復(fù)合神經(jīng)網(wǎng)絡(luò)的排水系統(tǒng)故障診斷研究_第1頁
基于復(fù)合神經(jīng)網(wǎng)絡(luò)的排水系統(tǒng)故障診斷研究_第2頁
基于復(fù)合神經(jīng)網(wǎng)絡(luò)的排水系統(tǒng)故障診斷研究_第3頁
基于復(fù)合神經(jīng)網(wǎng)絡(luò)的排水系統(tǒng)故障診斷研究_第4頁
基于復(fù)合神經(jīng)網(wǎng)絡(luò)的排水系統(tǒng)故障診斷研究_第5頁
已閱讀5頁,還剩3頁未讀, 繼續(xù)免費(fèi)閱讀

下載本文檔

版權(quán)說明:本文檔由用戶提供并上傳,收益歸屬內(nèi)容提供方,若內(nèi)容存在侵權(quán),請進(jìn)行舉報(bào)或認(rèn)領(lǐng)

文檔簡介

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

溫馨提示

  • 1. 本站所有資源如無特殊說明,都需要本地電腦安裝OFFICE2007和PDF閱讀器。圖紙軟件為CAD,CAXA,PROE,UG,SolidWorks等.壓縮文件請下載最新的WinRAR軟件解壓。
  • 2. 本站的文檔不包含任何第三方提供的附件圖紙等,如果需要附件,請聯(lián)系上傳者。文件的所有權(quán)益歸上傳用戶所有。
  • 3. 本站RAR壓縮包中若帶圖紙,網(wǎng)頁內(nèi)容里面會有圖紙預(yù)覽,若沒有圖紙預(yù)覽就沒有圖紙。
  • 4. 未經(jīng)權(quán)益所有人同意不得將文件中的內(nèi)容挪作商業(yè)或盈利用途。
  • 5. 人人文庫網(wǎng)僅提供信息存儲空間,僅對用戶上傳內(nèi)容的表現(xiàn)方式做保護(hù)處理,對用戶上傳分享的文檔內(nèi)容本身不做任何修改或編輯,并不能對任何下載內(nèi)容負(fù)責(zé)。
  • 6. 下載文件中如有侵權(quán)或不適當(dāng)內(nèi)容,請與我們聯(lián)系,我們立即糾正。
  • 7. 本站不保證下載資源的準(zhǔn)確性、安全性和完整性, 同時(shí)也不承擔(dān)用戶因使用這些下載資源對自己和他人造成任何形式的傷害或損失。

評論

0/150

提交評論