锅炉设计外文翻译燃煤锅炉的个案事故研究Word格式文档下载.docx
- 文档编号:18616415
- 上传时间:2022-12-29
- 格式:DOCX
- 页数:9
- 大小:71.59KB
锅炉设计外文翻译燃煤锅炉的个案事故研究Word格式文档下载.docx
《锅炉设计外文翻译燃煤锅炉的个案事故研究Word格式文档下载.docx》由会员分享,可在线阅读,更多相关《锅炉设计外文翻译燃煤锅炉的个案事故研究Word格式文档下载.docx(9页珍藏版)》请在冰豆网上搜索。
Vol.III,LNAI3683,Springer,Heidelberg,Germany,2005,pp.953-958.
MiningTemporalData:
ACoal-FiredBoilerCaseStudy
AndrewKusiakandAlexBurns
IntelligentSystemsLaboratory,IndustrialEngineering
3131SeamansCenter,TheUniversityofIowa
IowaCity,IA52242–1527,USA
andrew-kusiak@uiowa.edu
Abstract
Thispaperpresentsanapproachtocontrolpluggageofacoal-firedboiler.Theproposedapproachinvolvesstatistics,datapartitioning,parameterreduction,anddatamining.Theproposedapproachwastestedona750MWcommercialcoal-firedboileraffectedwithafoulingproblemthatleadstoboilerpluggagethatcausesunscheduledshutdowns.Therare-eventdetectionapproachpresentedinthepaperidentifiedseveralcriticaltime-baseddatasegmentsthatareindicativeoftheashpluggage.
1Introduction
Theabilitytopredictandavoidrareeventsintimeseriesdataisachallengethatcouldbeaddressedbydataminingapproaches.Difficultiesarisefromthefactthatoftenasignificantvolumeofdatadescribesnormalconditionsandonlyasmallamountofdatamaybeavailableforrareevents.Thisproblemisfurtherexacerbatedbythefactthattraditionaldataminingdoesnotaccountforthetimedependencyofthetemporaldata.Theapproachpresentedinthispaperovercomestheseconcernsbydefiningtime
windows.
Theapproachpresentedinthispaperisbasedonthetwomainconcepts.Thefirstisthatthedecision-treedata-miningalgorithmcapturesthesubtleparameterrelationshipsthatcausetherareeventtooccur[1].Thesecondconceptisthatpartitioningthedatausingtimewindowsprovidestheabilitytocaptureanddescribesequencesofeventsthatmaycausetherarefailure.
2EventDetectionProcedure
Inthecasestudydiscussedinthenextsectionrareeventscanbedetectedbyapplyingthefivestepprocedure.Thesefivestepsinclude:
Step1:
ParameterCategorization
Theparameterlistisdividedintotwocategories,responseparametersandimpactparameters.Responseparametersarethosethatchangevaluesduetoarareeventorafailure,e.g.,anairleakinapressurizedchamber.Impactparametersaredefinedasparametersthatareeitherdirectlyorindirectlycontrollableandmaycausetherareevent.Thesearetheparametersthatareofgreatestinterestforthedeterminationofrareevents.
Step2:
TimeSegmentation
Timesegmentationdealswithpartitioningandlabelingthedataintotimewindows(TWs).Atimewidowisdefinedasasetofobservationsinchronologicalorderthatdescribeaspecifiedamountofcontinuousobservations.Thisstepallowsthedataminingalgorithmstoaccountforthetemporalnatureofthedata.Themosteffectivemethodtosegmentthedataisbydetermining/estimatingtheapproximatedateoffailureandsetthatasthelastobservationofthefinaltimewindow.
Step3:
StatisticalandVisualAnalysis
Thisstepinvolvesstatisticalanalysisofthedataineachtimeperiodthatwasdesignatedinthepreviousstep.Processshifts,changesinvariation,andmeanshiftsinparametersarehelpfulinindicatingthattheappropriatetimewindowsandparameterswereselected.
Step4:
KnowledgeExtraction
DataminingalgorithmsdiscoverrelationshipsamongparametersandanoutcomeintheformofIF…THENrulesandotherconstructs(e.g.,decisiontables)[1],[5].Dataminingisnaturalextensionofmoretraditionaltoolssuchasneuralnetworks,multivariablealgorithms,ortraditionalstatistics.Inthedetectionofrareevents,thedecision-treeandrule-inductionalgorithmsareexploredfortwosignificantreasons.First,thealgorithmsgenerateexplicitknowledgeintheformunderstandablebyauser.Theuserisabletounderstandtheextractedknowledge,assessitsusefulness,andlearnnewandinterestingconcepts.Secondly,thedataminingalgorithmshavebeenshowntoproducehighlyaccurateknowledgeinmanydomains.
Step5:
AnalysisofKnowledgeandValidation
Thisstepdealswithvalidationoftheknowledgegeneratedbythedataminingalgorithm.Ifavalidationdatasetisavailableitshouldbeusedtovalidatetheaccuracyoftherules.Ifnosimilardataisavailablethenunuseddatafromtheanalysisora10-foldcross-validationcanbeutilized[6].
3PowerBoilerCaseStudy
Theapproachproposedinthisresearchwasappliedtopowerplantdata.Dataminingalgorithmsarewellsuitedforelectricpowerapplicationsthatproducehundredsofdatapointsatanytimeinstance.
Thiscasestudydealswithanashfoulingconditionthatcausesboilershutdownsseveraltimesayearonacommercial750MWtangentially-firedcoalboiler.Theashfoulingcausesabuildupofmaterialandpluggageinthereheatersectionoftheboiler.Oncethebuildupbecomessubstantialtheboilerperformanceisnegativelyaffected.Thisleadstothederatingandtheeventualshutdownoftheboiler.Thecleaningoftheboilerduringtheshutdownrequires1to3days.Thisproblemismademoredifficultbythefactthereisnomethodtodeterminethelevelofashbuildupwithoutshuttingdowntheboilertophysicallyinspectthearea.Furthermore,inanalysisallparameterswerewithinspecifications,sotherewasnoobvioussingleparameterthatiscausingthepluggage.Toinvestigatetheproblemconsideredinthispaper,datawascollectedon173differentboilerparameters.Thisincludedflows,pressures,temperatures,controls,demands,andsoon.Thedatawascollectedinone-minuteintervalsoverthecourseofthreemonths.Thedatacollectionbegandirectlyfollowingashutdownwherethereheatersectionoftheboilerhadnopluggage.Thecollectionperiodendedapproximatelythreemonthslaterwhentheboilerhadtobeshutdownforpluggageremoval.Thisdatasetcontainedover168,000observations.
Thelistof173parameters,whichincludedbothresponseandimpactparameters,wasanalyzed.Thelistwasreducedtoincludetwenty-siximpactparameters.Thisparametercategorizationandreductionwasaccomplishedwiththeassistanceofdomainexpertsaswellasstatisticalanalysissuchascorrelationandmultivariateanalysis.
Theinitialstepfortimesegmentingthedatawastodetermineanapproximatedateforthefailureevent.Inthisapplicationthefailureeventwasdefinedbythedatewhentheboilerwasderatedduetothepluggage.Thecauseoftheshutdownwasconfirmedthroughvisualinspectionoftheaffectedregion.Thisdatewasthensettobethelastdayofthefinaltimewindow(TW6).
Thewindowsweresettobeapproximatelyoneweeklong.Aweekwaschosenforseveralreasons.First,theboilerwasinspectedapproximatelyonemonthpriortoitsderating.Duringtheinspectionthereheatersectionoftheboilerwascompletelyfreeofash.Thisinformationprovidedtheknowledgethatthepluggagerequiredlesstheonemonthtomanifestitselftothepointofshutdown.Itwashypothesizedthatthepluggagerequiresseveraldaystobuildup.Basedonthisinformationoneweekwasdeemedtobeanadequatetimewindow.Oneweekalsoprovidedasufficientnumberofobservations(over10,000perwindow)forthedataminingalgorithms.
Usingthederatedateandaone-week-longtimewindow,thedatawasdividedintosixtimewindowsshowninFigure1.Timewindow1(TW1)wasincludedtoensurethattherewasadequatedatatodescribenormaloperatingconditions.
Thereappearsbeaprocessshiftbetweentimewindows3and5inFigures1.Thewesttiltdemonstratesameanshiftduringwindowthreeandthehotreheatsteamtemperaturedisplaysameanshiftaswellasalargeincreaseinvariationstartingintimewindowfourandculminatinginwindowfive.Theresultsofthisanalysisleadtothehypothesisthattheeventsthatleadtotheeventualpluggageoccurbetweentimewindowsthreeandfive.Italsoconfirmstheselectionofparametersandwindowsize.
Thedataminingapproachwasthenappliedtothedatasettopredictthepredefinedtimewindows(decisionparameter).Thealgorithmproducedasetofrulesthatdescribedtheparameterrelationshipsineachtimewindow.
Theknowledgeextractedbythealgorithmhadanoverall10-foldclassificationaccuracyof99.7%.Theconfusionmatrix(absoluteclassificationaccuracymatrix)isshowninFigure2.Thematrixdisplaystheactualvaluesandthevaluespredictedbytherulesduringthecross-validationprocess.
ItcanbeseenfromthedatainFigure2thattherearefewpredictedvaluesthatareoffbymorethanonetimewindowfromtheactualwindow.Theresultsprovidedintheconfusionmatrixprovideahighconfidenceintheproposedsolutionapproach.
Anothertestdatasetwasextractedfromtheweekfollowingtimewindow1andwaslabeledtimewindow2(TestTW2).Thelastportionofthedata(TestTW3)wasobtainedfromtheweekafterthegeneratorwasderatedandtheoutcomewaslabeledtimewindow6(TW6).Thetotaltestsetcontainedover30,000observations.
Therulesandknowledgethatwereextractedfromtheoriginaldatasetwerethentestedusingthetestdataset.Forpurposesofanalysistimewindows1–3wereconsiderednormalandtimewindow4–6wereconsideredfaulty.TheresultingconfusionmatrixisshowninFigure3.
Therulesaccuratelypredictedthenormalcases,buttheywerenotaseffectiveinpredictingthefaultcases.Thisismostlikelyexplainedbythefactthatthetestdatalabeled,timewindow6,wasextractedaftertheboilerhadbeenderated.Thederatingof
- 配套讲稿:
如PPT文件的首页显示word图标,表示该PPT已包含配套word讲稿。双击word图标可打开word文档。
- 特殊限制:
部分文档作品中含有的国旗、国徽等图片,仅作为作品整体效果示例展示,禁止商用。设计者仅对作品中独创性部分享有著作权。
- 关 键 词:
- 锅炉 设计 外文 翻译 燃煤 个案 事故 研究