大型風電機組故障模式統(tǒng)計分析及故障診斷一、本文概述Overviewofthisarticle隨著全球能源結構的轉(zhuǎn)變和可再生能源的大力發(fā)展，風能作為一種清潔、可再生的能源形式，正逐漸在世界范圍內(nèi)得到廣泛應用。大型風電機組作為風能發(fā)電的核心設備，其運行穩(wěn)定性和安全性對于整個風電系統(tǒng)的效率及壽命具有至關重要的影響。然而，風電機組在復雜的自然環(huán)境和多變的運行工況下，常常會受到各種因素的影響而發(fā)生故障，這不僅會影響風電場的正常發(fā)電，還會增加維修成本和運行風險。因此，對大型風電機組的故障模式進行統(tǒng)計分析，并開展故障診斷技術研究，對于提高風電機組的可靠性和運行效率，降低運維成本，具有十分重要的意義。Withthetransformationoftheglobalenergystructureandthevigorousdevelopmentofrenewableenergy,windenergy,asacleanandrenewableformofenergy,isgraduallybeingwidelyusedworldwide.Asthecoreequipmentofwindpowergeneration,thestabilityandsafetyoflarge-scalewindturbineshaveacrucialimpactontheefficiencyandlifespanoftheentirewindpowersystem.However,windturbinesoftenfailduetovariousfactorsincomplexnaturalenvironmentsandever-changingoperatingconditions.Thisnotonlyaffectsthenormalpowergenerationofwindfarms,butalsoincreasesmaintenancecostsandoperationalrisks.Therefore,conductingstatisticalanalysisofthefaultmodesoflargewindturbinesandconductingresearchonfaultdiagnosistechniquesisofgreatsignificanceforimprovingthereliabilityandoperationalefficiencyofwindturbines,reducingmaintenancecosts.本文旨在通過對大型風電機組的故障模式進行統(tǒng)計分析，揭示其常見故障類型、發(fā)生頻率及影響程度，進而為風電場的運維管理提供決策支持。本文還將探討和研究故障診斷技術在大型風電機組中的應用，包括傳統(tǒng)的故障診斷方法和基于大數(shù)據(jù)等新興技術的故障診斷方法，以期提高故障診斷的準確性和效率，為風電行業(yè)的可持續(xù)發(fā)展做出貢獻。Thisarticleaimstoconductstatisticalanalysisonthefaultmodesoflargewindturbines,revealtheircommonfaulttypes,frequencyofoccurrence,anddegreeofimpact,andprovidedecisionsupportfortheoperationandmaintenancemanagementofwindfarms.Thisarticlewillalsoexploreandstudytheapplicationoffaultdiagnosistechnologyinlargewindturbines,includingtraditionalfaultdiagnosismethodsandfaultdiagnosismethodsbasedonemergingtechnologiessuchasbigdata,inordertoimprovetheaccuracyandefficiencyoffaultdiagnosisandcontributetothesustainabledevelopmentofthewindpowerindustry.在接下來的章節(jié)中，本文將首先介紹大型風電機組的基本結構和工作原理，然后對其常見故障類型、發(fā)生原因及影響進行分析和歸類。在此基礎上，本文將重點探討故障診斷技術的研究現(xiàn)狀和發(fā)展趨勢，并通過案例分析來驗證不同故障診斷方法在實際應用中的效果。本文將對全文進行總結，并提出未來研究方向和建議。Inthefollowingchapters,thisarticlewillfirstintroducethebasicstructureandworkingprincipleoflargewindturbines,andthenanalyzeandclassifytheircommonfaulttypes,causes,andimpacts.Onthisbasis,thisarticlewillfocusonexploringtheresearchstatusanddevelopmenttrendsoffaultdiagnosistechnology,andverifytheeffectivenessofdifferentfaultdiagnosismethodsinpracticalapplicationsthroughcaseanalysis.Thisarticlewillsummarizetheentiretextandproposefutureresearchdirectionsandsuggestions.二、大型風電機組常見故障模式Commonfaultmodesoflargewindturbines大型風電機組作為復雜的機電系統(tǒng)，其運行過程中可能遇到的故障模式多種多樣。這些故障模式不僅影響風電機組的運行效率，還可能對設備的安全性和使用壽命產(chǎn)生嚴重影響。因此，對大型風電機組的常見故障模式進行統(tǒng)計分析，對于提高風電系統(tǒng)的可靠性和經(jīng)濟性具有重要意義。Asacomplexelectromechanicalsystem,largewindturbinesmayencountervariousfaultmodesduringtheiroperation.Thesefaultmodesnotonlyaffecttheoperationalefficiencyofwindturbines,butmayalsohaveaseriousimpactonthesafetyandservicelifeofequipment.Therefore,statisticalanalysisofcommonfaultmodesoflargewindturbinesisofgreatsignificanceforimprovingthereliabilityandeconomyofwindpowersystems.機械故障：機械故障是風電機組中最常見的故障模式之一，主要包括齒輪箱故障、軸承故障和葉片故障等。齒輪箱故障通常表現(xiàn)為齒輪磨損、斷裂或軸承故障等，這些故障會導致能量轉(zhuǎn)換效率降低，甚至引起機組停機。軸承故障則主要表現(xiàn)為軸承磨損、潤滑不良和過熱等，這些問題會嚴重影響機組的穩(wěn)定性和壽命。葉片故障則通常由于極端天氣條件、材料疲勞或設計缺陷等原因引起，可能導致葉片斷裂或失衡。Mechanicalfailure:Mechanicalfailureisoneofthemostcommonfailuremodesinwindturbines,mainlyincludinggearboxfailure,bearingfailure,andbladefailure.Gearboxfailuresusuallymanifestasgearwear,fracture,orbearingfailure,whichcanleadtoadecreaseinenergyconversionefficiencyandevencauseunitshutdown.Bearingfailuresmainlymanifestasbearingwear,poorlubrication,andoverheating,whichcanseriouslyaffectthestabilityandservicelifeoftheunit.Bladefailuresareusuallycausedbyextremeweatherconditions,materialfatigue,ordesigndefects,whichmayleadtobladefractureorimbalance.電氣故障：電氣故障也是風電機組中常見的故障模式之一，主要包括發(fā)電機故障、變流器故障和電纜故障等。發(fā)電機故障可能由繞組絕緣損壞、軸承磨損等原因引起，這些問題會導致機組發(fā)電效率低下，甚至停機。變流器故障則主要表現(xiàn)為功率器件損壞、控制策略不當?shù)龋@些問題會影響機組的電能質(zhì)量。電纜故障則可能由于環(huán)境因素、過載或老化等原因引起，可能導致機組運行不穩(wěn)定或停機。Electricalfailure:Electricalfailureisalsooneofthecommonfailuremodesinwindturbines,mainlyincludinggeneratorfailure,inverterfailure,andcablefailure.Generatorfailuresmaybecausedbyinsulationdamagetowindings,bearingwear,andotherreasons,whichcanleadtolowpowergenerationefficiencyandevenshutdownoftheunit.Themainmanifestationsofinverterfaultsarepowerdevicedamage,impropercontrolstrategies,etc.,whichcanaffectthepowerqualityoftheunit.Cablefaultsmaybecausedbyenvironmentalfactors,overload,oraging,whichmayleadtounstableoperationorshutdownoftheunit.控制系統(tǒng)故障：控制系統(tǒng)是風電機組的核心部分，其故障模式也多種多樣。常見的控制系統(tǒng)故障包括傳感器故障、執(zhí)行器故障和控制器故障等。傳感器故障可能導致機組無法準確感知運行狀態(tài)，從而影響控制策略的有效性。執(zhí)行器故障則可能導致機組無法準確執(zhí)行控制指令，影響機組的運行穩(wěn)定性。控制器故障則可能由于軟件缺陷、硬件故障等原因引起，可能導致機組無法正常運行。Controlsystemfailure:Thecontrolsystemisthecorepartofwindturbines,anditsfailuremodesarealsodiverse.Commoncontrolsystemfailuresincludesensorfailures,actuatorfailures,andcontrollerfailures.Sensorfailuresmaycausetheunittobeunabletoaccuratelyperceivetheoperatingstatus,therebyaffectingtheeffectivenessofcontrolstrategies.Anactuatormalfunctionmaycausetheunittobeunabletoaccuratelyexecutecontrolinstructions,affectingtheoperationalstabilityoftheunit.Controllerfailuresmaybecausedbysoftwaredefects,hardwarefailures,andotherreasons,whichmaycausetheunittomalfunction.大型風電機組的常見故障模式主要包括機械故障、電氣故障和控制系統(tǒng)故障等。為了有效應對這些故障模式，需要加強對風電機組的運行維護和故障診斷技術的研究與應用，以提高風電系統(tǒng)的可靠性和經(jīng)濟性。Thecommonfailuremodesoflargewindturbinesmainlyincludemechanicalfailures,electricalfailures,andcontrolsystemfailures.Inordertoeffectivelyrespondtothesefaultmodes,itisnecessarytostrengthentheresearchandapplicationofoperation,maintenance,andfaultdiagnosistechnologiesforwindturbines,inordertoimprovethereliabilityandeconomyofwindpowersystems.三、故障模式統(tǒng)計分析方法Statisticalanalysismethodsforfaultmodes在大型風電機組故障模式統(tǒng)計分析中，我們采用了多種方法以確保數(shù)據(jù)的全面性和準確性。我們采用了基于歷史數(shù)據(jù)的統(tǒng)計分析方法。這種方法涉及收集風電機組在過去幾年中發(fā)生的各種故障數(shù)據(jù)，包括故障類型、發(fā)生時間、發(fā)生頻率等。通過對這些數(shù)據(jù)進行深入分析，我們能夠識別出最常見的故障模式以及它們發(fā)生的規(guī)律。Inthestatisticalanalysisoffaultmodesinlargewindturbines,wehaveadoptedvariousmethodstoensurethecomprehensivenessandaccuracyofthedata.Weadoptedastatisticalanalysismethodbasedonhistoricaldata.Thismethodinvolvescollectingvariousfaultdataofwindturbinesthathaveoccurredinthepastfewyears,includingfaulttypes,occurrencetime,frequency,etc.Byconductingin-depthanalysisofthesedata,wecanidentifythemostcommonfaultmodesandtheirpatternsofoccurrence.我們采用了基于故障樹的分析方法。這種方法通過構建一個故障樹，將各種故障模式與它們之間的邏輯關系清晰地展示出來。我們根據(jù)風電機組的結構和功能，將各種可能的故障模式分解為更小的子故障，并分析它們之間的因果關系。這種方法有助于我們更深入地理解故障模式的復雜性，并找出導致故障的根本原因。Weadoptedafaulttreebasedanalysismethod.Thismethodclearlydisplaysvariousfaultmodesandtheirlogicalrelationshipsbyconstructingafaulttree.Wedecomposevariouspossiblefaultmodesintosmallersubfaultsbasedonthestructureandfunctionofwindturbines,andanalyzetheircausalrelationships.Thismethodhelpsustogainadeeperunderstandingofthecomplexityoffaultmodesandidentifytherootcauseofthefailure.我們還采用了基于統(tǒng)計模型的方法，如貝葉斯網(wǎng)絡、隱馬爾可夫模型等。這些統(tǒng)計模型可以根據(jù)歷史數(shù)據(jù)學習故障模式的發(fā)生概率和轉(zhuǎn)移規(guī)律，從而預測未來可能發(fā)生的故障。通過這種方法，我們可以提前發(fā)現(xiàn)潛在的問題，并采取相應的預防措施，避免故障的發(fā)生。Wealsoadoptedmethodsbasedonstatisticalmodels,suchasBayesiannetworks,hiddenMarkovmodels,etc.Thesestatisticalmodelscanlearntheprobabilityandtransitionpatternsoffaultmodesbasedonhistoricaldata,therebypredictingpossiblefuturefaults.Throughthismethod,wecanidentifypotentialproblemsinadvanceandtakecorrespondingpreventivemeasurestoavoidtheoccurrenceoffaults.在數(shù)據(jù)分析過程中，我們還注重數(shù)據(jù)的可視化和交互式探索。通過使用各種圖表和可視化工具，我們能夠更直觀地展示故障模式的分布和趨勢，方便研究人員和工程師快速地獲取關鍵信息。我們也鼓勵用戶通過交互式探索方式挖掘數(shù)據(jù)中的潛在價值，提出新的假設和觀點。Intheprocessofdataanalysis,wealsofocusondatavisualizationandinteractiveexploration.Byusingvariouschartsandvisualizationtools,wecanmoreintuitivelydisplaythedistributionandtrendoffaultmodes,facilitatingresearchersandengineerstoquicklyobtainkeyinformation.Wealsoencourageuserstoexplorethepotentialvalueofdatathroughinteractiveexploration,andproposenewhypothesesandperspectives.我們采用了多種方法對大型風電機組的故障模式進行了統(tǒng)計分析。這些方法不僅幫助我們?nèi)媪私饬斯收夏Ｊ降奶卣骱鸵?guī)律，還為故障診斷和預防措施的制定提供了有力的支持。Wehaveusedvariousmethodstostatisticallyanalyzethefaultmodesoflargewindturbines.Thesemethodsnotonlyhelpuscomprehensivelyunderstandthecharacteristicsandpatternsoffaultmodes,butalsoprovidestrongsupportforthedevelopmentoffaultdiagnosisandpreventivemeasures.四、故障診斷技術Faultdiagnosistechnology大型風電機組的故障診斷是確保風電場長期穩(wěn)定運行的關鍵環(huán)節(jié)。隨著技術的進步，目前已有多種先進的故障診斷技術應用于風電機組。這些技術主要包括基于振動分析的方法、基于溫度監(jiān)測的方法、基于聲學診斷的方法以及基于數(shù)據(jù)驅(qū)動的故障診斷方法等。Thefaultdiagnosisoflargewindturbinesisacrucialstepinensuringthelong-termstableoperationofwindfarms.Withtheadvancementoftechnology,variousadvancedfaultdiagnosistechnologieshavebeenappliedtowindturbines.Thesetechnologiesmainlyincludemethodsbasedonvibrationanalysis,temperaturemonitoring,acousticdiagnosis,anddata-drivenfaultdiagnosis.基于振動分析的方法通過監(jiān)測風電機組關鍵部件的振動信號，結合信號處理技術提取故障特征，進而判斷故障類型。這種方法對于齒輪箱、軸承等旋轉(zhuǎn)部件的故障診斷尤為有效。通過振動傳感器采集到的數(shù)據(jù)，可以分析出部件的磨損、裂紋等故障信息。Themethodbasedonvibrationanalysismonitorsthevibrationsignalsofkeycomponentsofwindturbines,combinessignalprocessingtechnologytoextractfaultcharacteristics,andthendeterminesthetypeoffault.Thismethodisparticularlyeffectiveforfaultdiagnosisofrotatingcomponentssuchasgearboxesandbearings.Thedatacollectedbyvibrationsensorscananalyzethefaultinformationofcomponentssuchaswearandcracks.基于溫度監(jiān)測的方法則通過實時監(jiān)測風電機組各部件的溫度變化，發(fā)現(xiàn)異常溫升現(xiàn)象，從而推斷出可能的故障點。這種方法特別適用于發(fā)電機、變流器等熱特性較為明顯的部件。Themethodbasedontemperaturemonitoringdetectsabnormaltemperaturerisebymonitoringthetemperaturechangesofvariouscomponentsofthewindturbineinrealtime,therebyinferringpossiblefaultpoints.Thismethodisparticularlysuitableforcomponentswithobviousthermalcharacteristicssuchasgeneratorsandinverters.聲學診斷方法則通過采集風電機組運行時的聲音信號，利用聲學特征提取和模式識別技術來判斷故障。例如，通過監(jiān)聽齒輪箱或軸承的異響，可以及時發(fā)現(xiàn)潛在的故障。Theacousticdiagnosticmethodusesacousticfeatureextractionandpatternrecognitiontechniquestoidentifyfaultsbycollectingsoundsignalsduringtheoperationofwindturbines.Forexample,bymonitoringtheabnormalnoiseofthegearboxorbearings,potentialfaultscanbedetectedinatimelymanner.基于數(shù)據(jù)驅(qū)動的故障診斷方法近年來得到了廣泛的關注。這種方法利用大量的風電機組運行數(shù)據(jù)，通過機器學習、深度學習等算法，建立故障診斷模型。通過對模型的不斷訓練和優(yōu)化，可以實現(xiàn)對風電機組故障的準確診斷。Thedata-drivenfaultdiagnosismethodhasreceivedwidespreadattentioninrecentyears.Thismethodutilizesalargeamountofwindturbineoperatingdataandestablishesafaultdiagnosismodelthroughalgorithmssuchasmachinelearninganddeeplearning.Bycontinuouslytrainingandoptimizingthemodel,accuratediagnosisofwindturbinefaultscanbeachieved.在實際應用中，這些故障診斷技術往往需要結合使用，以充分利用各自的優(yōu)點，提高故障診斷的準確性和效率。隨著物聯(lián)網(wǎng)、云計算等技術的發(fā)展，未來的故障診斷技術將更加智能化、網(wǎng)絡化，為風電場的運維管理提供更加便捷、高效的支持。Inpracticalapplications,thesefaultdiagnosistechniquesoftenneedtobecombinedtofullyutilizetheirrespectiveadvantagesandimprovetheaccuracyandefficiencyoffaultdiagnosis.WiththedevelopmentoftechnologiessuchastheInternetofThingsandcloudcomputing,futurefaultdiagnosistechnologieswillbecomemoreintelligentandnetworked,providingmoreconvenientandefficientsupportfortheoperationandmanagementofwindfarms.五、案例分析Caseanalysis為了進一步驗證和展示大型風電機組故障模式統(tǒng)計分析及故障診斷方法的有效性，本章節(jié)將通過幾個實際案例進行深入分析。Inordertofurtherverifyanddemonstratetheeffectivenessofstatisticalanalysisoffaultmodesandfaultdiagnosismethodsforlargewindturbines,thischapterwillconductin-depthanalysisthroughseveralpracticalcases.在某風電場，一臺5兆瓦風電機組在運行過程中出現(xiàn)了異常振動和噪音增大的現(xiàn)象。通過采集該機組的振動數(shù)據(jù)和運行參數(shù)，利用故障診斷系統(tǒng)進行分析，最終確定故障原因為齒輪箱內(nèi)部軸承損壞。根據(jù)故障模式統(tǒng)計分析的結果，齒輪箱軸承故障在該風電場屬于常見故障類型，與齒輪箱設計、潤滑不當以及運行環(huán)境等多種因素有關。通過及時更換軸承，機組恢復了正常運行。Inacertainwindfarm,a5-megawattwindturbineunitexperiencedabnormalvibrationandincreasednoiseduringoperation.Bycollectingvibrationdataandoperatingparametersoftheunit,andusingafaultdiagnosissystemforanalysis,itwasultimatelydeterminedthatthecauseofthefaultwasdamagetotheinternalbearingsofthegearbox.Accordingtothestatisticalanalysisoffaultmodes,thebearingfailureofthegearboxisacommontypeoffaultinthewindfarm,whichisrelatedtovariousfactorssuchasgearboxdesign,improperlubrication,andoperatingenvironment.Bytimelyreplacingthebearings,theunitresumednormaloperation.在某風電場，一臺2兆瓦風電機組在運行過程中突然停機，經(jīng)檢查發(fā)現(xiàn)發(fā)電機內(nèi)部短路。利用故障診斷系統(tǒng)對發(fā)電機歷史運行數(shù)據(jù)進行分析，發(fā)現(xiàn)發(fā)電機在運行過程中存在溫度異常升高的現(xiàn)象。結合故障模式統(tǒng)計分析的結果，認為發(fā)電機絕緣材料老化是導致短路的主要原因。針對這一問題，風電場加強了對發(fā)電機溫度的監(jiān)控，并定期對發(fā)電機進行絕緣性能檢測，有效預防了類似故障的發(fā)生。Atacertainwindfarm,a2-megawattwindturbinesuddenlyshutdownduringoperation.Uponinspection,itwasfoundthattherewasaninternalshortcircuitinthegenerator.Byusingafaultdiagnosissystemtoanalyzethehistoricaloperatingdataofthegenerator,itwasfoundthattherewasanabnormaltemperatureincreaseduringtheoperationofthegenerator.Basedonthestatisticalanalysisoffaultmodes,itisbelievedthatagingofgeneratorinsulationmaterialsisthemaincauseofshortcircuits.Inresponsetothisissue,windfarmshavestrengthenedthemonitoringofgeneratortemperatureandregularlyconductedinsulationperformancetestsongenerators,effectivelypreventingsimilarfaultsfromoccurring.在某風電場，多臺風電機組出現(xiàn)了頻繁停機的現(xiàn)象，經(jīng)檢查發(fā)現(xiàn)控制系統(tǒng)存在故障。通過采集控制系統(tǒng)相關數(shù)據(jù)和日志信息，利用故障診斷系統(tǒng)進行分析，發(fā)現(xiàn)控制系統(tǒng)軟件存在漏洞，導致機組在特定環(huán)境條件下出現(xiàn)誤判和誤動作。針對這一問題，風電場對控制系統(tǒng)軟件進行了升級和優(yōu)化，提高了機組的運行穩(wěn)定性和可靠性。Atacertainwindfarm,multiplewindturbinesexperiencedfrequentshutdowns,anduponinspection,itwasfoundthattherewasamalfunctioninthecontrolsystem.Bycollectingrelevantdataandloginformationfromthecontrolsystemandanalyzingitusingafaultdiagnosissystem,itwasfoundthattherewerevulnerabilitiesinthecontrolsystemsoftware,whichledtomisjudgmentandmisoperationoftheunitunderspecificenvironmentalconditions.Inresponsetothisissue,thewindfarmhasupgradedandoptimizedthecontrolsystemsoftware,improvingtheoperationalstabilityandreliabilityoftheunits.通過以上三個案例的分析，可以看到大型風電機組故障模式統(tǒng)計分析及故障診斷方法在實際應用中具有重要價值。通過對機組運行數(shù)據(jù)和故障信息的深入挖掘和分析，可以準確判斷故障原因和類型，為風電場的運維管理提供有力支持。通過加強故障模式統(tǒng)計分析工作，還可以為風電設備的研發(fā)設計和制造提供改進依據(jù)，推動風電行業(yè)的技術進步和可持續(xù)發(fā)展。Throughtheanalysisoftheabovethreecases,itcanbeseenthatthestatisticalanalysisoffaultmodesandfaultdiagnosismethodsforlargewindturbineshaveimportantvalueinpracticalapplications.Throughin-depthminingandanalysisofunitoperationdataandfaultinformation,thecausesandtypesoffaultscanbeaccuratelydetermined,providingstrongsupportfortheoperationandmaintenancemanagementofwindfarms.Bystrengtheningthestatisticalanalysisoffaultmodes,itcanalsoprovideimprovementbasisfortheresearch,development,design,andmanufacturingofwindpowerequipment,promotingtechnologicalprogressandsustainabledevelopmentinthewindpowerindustry.六、結論與展望ConclusionandOutlook經(jīng)過對大型風電機組故障模式進行深入的統(tǒng)計分析及故障診斷研究，本文得出了一系列重要結論。風電機組的故障模式呈現(xiàn)出多樣性和復雜性，其中機械故障、電氣故障和控制系統(tǒng)故障是最主要的故障類型。通過對故障數(shù)據(jù)的統(tǒng)計分析，我們發(fā)現(xiàn)不同故障模式之間存在一定的關聯(lián)性和相互影響，這為故障預警和診斷提供了重要的參考依據(jù)。本文還提出了一系列有效的故障診斷方法，包括基于振動信號分析、溫度監(jiān)測和功率曲線對比等，這些方法在實際應用中取得了良好的效果。Afterconductingin-depthstatisticalanalysisandfaultdiagnosisresearchonthefaultmodesoflargewindturbines,thisarticlehasdrawnaseriesofimportantconclusions.Thefaultmodesofwindturbinesexhibitdiversityandcomplexity,amongwhichmechanicalfaults,electricalfaults,andcontrolsystemfaultsarethemostcommontypesoffaults.Throughstatisticalanalysisoffaultdata,wefoundthatthereisacertaincorrelationandmutualinfluencebetweendifferentfaultmodes,whichprovidesimportantreferencebasisforfaultwarninganddiagnosis.Thisarticlealsoproposesaseriesofeffectivefaultdiagnosismethods,includingvibrationsignalanalysis,temperaturemonitoring,andpowercurvecomparison,whichhaveachievedgoodresultsinpracticalapplications.然而，目前的研究還存在一些不足和局限性。故障數(shù)據(jù)的收集和處理仍然面臨一定的挑戰(zhàn)，尤其是在故障發(fā)生初期難以準確捕捉和記錄相關數(shù)據(jù)。雖然本文提出了一些有效的故障診斷方法，但仍有待進一步提高其準確性和可靠性，以滿足實際工程應用的需求。However,therearestillsomeshortcomingsandlimitationsincurrentresearch.Thecollectionandprocessingoffaultdatastillfacecertainchallenges,especiallyintheearlystagesoffaultoccurrence,whereitisdifficulttoaccuratelycaptureandrecordrelevantdata.Althoughthisarticleproposessomeeffectivefaultdiagnosismethods,th