基于支持向量機的冷水機組故障檢測與診斷優(yōu)化研究基于支持向量機的冷水機組故障檢測與診斷優(yōu)化研究

摘要：冷水機組是現(xiàn)代空調(diào)系統(tǒng)的重要組成部分，其穩(wěn)定運行對于舒適的室內(nèi)環(huán)境至關(guān)重要。然而，冷水機組故障頻繁發(fā)生，給用戶帶來諸多不便和經(jīng)濟損失。為此，本文基于支持向量機(SupportVectorMachine,SVM)算法，針對冷水機組故障檢測與診斷進行了優(yōu)化研究。首先，通過數(shù)據(jù)采集和處理，建立了包括機組水流量、壓縮機電流、冷水溫度等在內(nèi)的故障檢測模型，實現(xiàn)了故障信號的采集和處理。其次，針對模型過擬合的問題，采用LASSO算法對模型進行優(yōu)化，加強了模型的泛化能力。最后，通過對真實數(shù)據(jù)的測試和對比分析，驗證了基于SVM算法的冷水機組故障檢測模型的有效性和優(yōu)越性。

關(guān)鍵詞：支持向量機；冷水機組；故障檢測；LASSO算法；優(yōu)化研究

Abstract:Chilledwaterunitsareimportantcomponentsofmodernairconditioningsystems.Thestableoperationofchilledwaterunitsiscrucialforacomfortableindoorenvironment.However,chillerfailuresoccurfrequently,causinginconvenienceandeconomiclossestousers.Therefore,basedontheSupportVectorMachine(SVM)algorithm,thispaperoptimizesthechillerfaultdetectionanddiagnosis.Firstly,bycollectingandprocessingdata,thefaultdetectionmodelincludingwaterflowrate,compressorcurrent,andchilledwatertemperatureisestablishedtorealizethecollectionandprocessingoffaultsignals.Secondly,tosolvetheproblemofoverfitting,theLASSOalgorithmisusedtooptimizethemodel,enhancingthemodel'sgeneralizationability.Finally,throughtestingandcomparativeanalysisofrealdata,theeffectivenessandsuperiorityofthechillerfaultdetectionmodelbasedontheSVMalgorithmareverified.

Keywords:supportvectormachines;chilledwaterunits;faultdetection;LASSOalgorithm;optimizationresearcInrecentyears,theuseofchilledwaterunitshasbecomeincreasinglycommoninvariousfields,suchasairconditioningandrefrigerationsystems.However,thecomplexityofthesesystemsandthevarietyofpossiblefaultsmakeitdifficulttomanuallydetectanddiagnosefaults.

Toaddressthisissue,theresearchpresentedinthispaperproposesafaultdetectionmodelbasedonthesupportvectormachine(SVM)algorithm.First,themodelcollectsandprocessesfaultsignalsfromthechilledwaterunit,includingtemperature,pressure,andflowratedata.ThesesignalsarethenusedtotraintheSVMalgorithmtoidentifydifferenttypesoffaults.

Onechallengeindevelopingafaultdetectionmodelistheproblemofoverfitting,wherethemodelistoocloselyfittedtothetrainingdataandfailstogeneralizetonewdata.Toovercomethischallenge,theLASSOalgorithmisappliedtooptimizetheSVMmodelbyselectingthemostimportantfeaturesandreducingtheimpactofirrelevantorredundantdata.Thisapproachimprovesthemodel'sgeneralizationabilityandreducestheriskofoverfitting.

Theproposedfaultdetectionmodelistestedandcomparedagainstothermethodsusingrealdatafromachilledwaterunit.TheresultsdemonstratetheeffectivenessandsuperiorityoftheSVM-basedmodel,withhigheraccuracyandfasterdetectionoffaultscomparedtoothermethods.

Inconclusion,thefaultdetectionmodelbasedontheSVMalgorithmandoptimizedbytheLASSOalgorithmoffersapromisingapproachtoimprovethereliabilityandefficiencyofchilledwaterunits.FurtherresearchcouldexploretheintegrationofthismodelwithothercontrolandmonitoringsystemstoenhancefaultdiagnosisandmanagementinthesesystemsOnepotentialareaoffurtherresearchistheapplicationoftheSVM-basedfaultdetectionmodeltoothertypesofHVACsystems,suchasairhandlingunits,rooftopunits,orhydronicsystems.EachofthesesystemshasuniquecharacteristicsandoperatingconditionsthatmayrequiremodificationstotheSVMmodel.Additionally,researchcouldexploretheeffectivenessofcombiningtheSVMmodelwithothermachinelearningalgorithmsorexpertsystemstoimprovefaultdiagnosisanddecision-making.

Anotherareaforfutureresearchistheintegrationofthefaultdetectionmodelwithpredictivemaintenancestrategies.Byaccuratelydiagnosingfaultsandpredictingwhentheyarelikelytooccur,theSVM-basedmodelcouldenableproactivemaintenanceandreducetheriskofequipmentfailure.Thiscouldresultinsignificantcostsavingsandincreasedequipmentlifespan.

Finally,researchcouldexaminetheimpactoftheSVM-basedfaultdetectionmodelonenergyefficiencyandenvironmentalsustainabilityinHVACsystems.Byquicklyidentifyinganddiagnosingfaults,themodelcouldpreventenergywasteandreducegreenhousegasemissionsassociatedwithunnecessaryequipmentoperationorreplacement.Thiscouldalignwithbroadersustainabilitygoalsandregulationsrelatedtobuildingenergyefficiency.

Overall,theSVM-basedfaultdetectionmodeloptimizedbytheLASSOalgorithmoffersapromisingapproachtoimprovingthereliabilityandefficiencyofchilledwaterunits.FurtherresearchanddevelopmentcouldenhanceitsapplicabilityandimpactinHVACsystemsandsupportthebroadergoalsofsustainabilityandenergyefficiencyFutureresearchanddevelopmentinthefieldofHVACsystemscouldfocusonimprovingandexpandingtheapplicationoftheSVM-basedfaultdetectionmodel.Oneareaofimprovementcouldbeincreasingtheaccuracyofthemodelbyincludingmorefeaturesanddatasets.Forexample,additionalsensordatasuchasflowratesandpressurelevelscouldbeusedtoenhancethemodel'sabilitytodetectfaults.

Anotherareaforfurtherexplorationistheintegrationofmachinelearningalgorithmswithbuildingautomationsystems.Thiswouldenableamoreautomatedapproachtofaultdetectionandmaintenance,wherethemachinelearningalgorithmscouldautomaticallyadjustthesystemsettingstopreventormitigatefaults.

Additionally,researchcouldfocusonthedevelopmentofmoresustainableandefficientHVACsystems.Forexample,theuseofrenewableenergysourcessuchassolarorgeothermalpowercouldreducetheenvironmentalimpactofHVACsystems.Furthermore,thedesignofbuildingsthemselvescouldbeoptimizedtoreducetheneedforHVACsystemsthroughimprovedinsulationandnaturalventilation.

Inconclusion,theSVM-basedfaultdetectionmodeloptimizedbytheLASSOalgorithmisapromisingtoolforimprovingthereliabilityandefficiencyofchilledwaterunitsinHVACsystems.Furtherresearchanddevelopmentcanenhancetheapplicabilityofthismodelandsupportbroadersustainabilityandenergyefficiencygoa