外文翻译-基于神经网络的逻辑故障监测的PLC控制系统

英文原文PLCcontrollogicerrormonitoringandpredictionusingNeuralNetworkAbstractThispaperreviewsmonitoringanderrorpredictionofPLCprogramusingNeuralNetwork.InthePLCdevicecontrolledmanufacturingline,PLCprogramholdsplaceofunderlyingcomponent.Itbecomescontrollingmechanism.Thelevelofautomationintheproductionlinereliesoncontrolmechanismpracticed.Inthemodernmanufacturing,PLCdevicescanhandlewholeproductionlinegiventhatstructuredandsmartPLCprogramisexecuted.Inotherwords,PLCprogramcanmanagewholeprocessstructureconsistingsetofprocedures.WepresentamethodtomonitorPLCprogramandPLCerrorpredictionitusingneuralnetwork.Theneuralnetworkmethodbeingpredictiveinnature,itrigorouslycanmonitorprocesssignalsfromsensors,sensedduringoperationofPLCdevicesorexecutionofPLCprogram.Subsequently,aneuralnetworkalgorithmpracticedfortheanalysisofsignals.Inthisway,thoroughmonitoringofPLCprogramcanfindpossibleerrorsfromtemporalparameters(e.g.Voltage,biasetc).Inaddition,possiblealterationsinprogramandirregularitiescanbeminimized.Thatcanresult,easilytouseinfaultdetection,maintenance,anddecisionsupportinmanufacturingorganization.Similarly,itcanlessendown-timeofmachinesandpreventpossiblerisks.Keywords:PLC,ArtificialNeuralNetwork(ANN),Fault-detection,Errorprediction,Monitoring.1.IntroductionInthemodernmanufacturing,thePLCiswell-adoptedtoarangeofautomationtasks.Thesearetypicallyindustrialprocesseswherechangestothesystemwouldbeexpectedduringitsoperationallifeandtheproductionsystemsthatfeaturecostofmaintainingisrelativelyhigherthancostofautomation1.PLCisspecial-purposecomputer,whichisdesignedformultipleinputandoutputarrangements,extendedtemperatureranges,immunitytoelectricalnoise,andresistancetovibrationandimpact.ThereasonbehindincreasingpopularityofPLC(ProgrammableLogicController)isflexibilityincontrol;thepossiblechangesinmanufacturingcontrollingareperformedthroughPLCprogram.ThePLCprogramdeterminesautomationlevelofamanufacturingindustry.Inotherwords,thewholeprocessstructureofproductionlinecanbemodeledandcontrolledbyprovidingsetofinstructionstoPLC.Inthisway,PLCprogrambecomesunderlyingcomponentofmodernmanufacturing.However,becauseofPLCnonflexibleprogrammingsystemrelativetohighlevellanguages,theirabilityinfaultdetectionanddiagnosisislimited.ThecontinuousmonitoringofPLCprogramisvitaltodecreasemachinedown-times,safety-criticalreasonsandpreventpotentialrisks.ThediagnosisofPLCprogrambecomesdifficultbecauseofdatacharacteristicinvolvedinprocess:analoganddiscrete2.PLCdevicesexecuteprogramsscanningcontinuouslyandoperateinvolvedmachinessendinginstructionsasI/Ointhediscreteordigitalformat.However,pressure,temperature,flow,andweightsareoftenrepresentedwithintegervalues.Hence,inputandoutputsignalsarerepresentedineitherbinaryorintegervalues;therearealwayschancesofalterationsinthevaluesintherealtimerunningproductionline.Thatis,theoriginallysoundPLCprogrammaybehaveabnormallyduetothechangesininputandoutputvalues.Usingneuralnetworkforfaultdiagnosisisnotcommonasforvisionorspeechprocessing,howevermanysuccessfulapplicationshavebeenreportednotably3.Annsareaformofartificialintelligence,which,bymeansoftheirarchitecture,attempttosimulatethebiologicalstructureofthehumanbrainandnervoussystem.Althoughtheconceptofartificialneuronswasfirstintroducedin1943,researchintoapplicationsofAnnshasblossomedsincetheintroductionoftheback-propagationtrainingalgorithmforfeed-forwardAnnsin19864.Annsmaythusbeconsideredarelativelynewtoolinthefieldofpredictionandforecasting.Whenfeed-forwardAnnsareusedforpredictionandforecasting,themodelingphilosophyemployedissimilartothatusedinthedevelopmentofmoreconventionalstatisticalmodels.Inbothcases,thepurposeofthemodelistocapturetherelationshipbetweenahistoricalsetofmodelinputsandcorrespondingoutputs.Thisisachievedbyrepeatedlypresentingexamplesoftheinput/outputrelationshiptothemodelandadjustingthemodelcoefficientsinanattempttominimizeanerrorfunctionbetweenthehistoricaloutputsandtheoutputspredictedbythemodel.AlthoughsomeANNmodelsarenotsignificantlydifferentfromanumberofstandardstatisticalmodels,theyareextremelyvaluableastheybelongtotheclassofdatadrivenapproaches,whereasconventionalstatisticalmethodsaremodeldriven.Intheformer,thedataareusedtodeterminethestructureofthemodelaswellastheunknownmodelparameters.TheuseofANNmodelsmaythusovercomethelimitationsofthetraditionalmethods.Particularly,itisfoundsuitableforpredictionsinceneuralnetworksarebestatidentifyingpatternsortrendsindata.Inourwork,wedesignaneuralnetworkwhichistrainedusingbackpropagationlearningalgorithm.ThetrainednetworkisusedtopredictvalidPLCprogramInputandoutput(I/O)valuesanditspattern.Attheend,theperformanceofthenetworkinpredictingtheseprocessparametersisstudied.2.LiteraturereviewMoreandmoreresearchersandindustrialpartnersareattractedtothisarea,faultdetectionandmonitoringsystem.Sofar,somerelevantdiagnosticmethodshavebeenproposed.W.Hu,M.Schroeder,&A.G.Starr5haveproposedknowledge-basedreal-timePLCdiagnosissystem.Theirworkisfocusedonacquiringknowledgefromthepneumatic&hydrauliccircuitdiagramsandPLCprogram.Later,simply,theyretrievePLC-dataandidentifypossiblefaults.Intheprocessobservationandfaultdetection,TordAlenjung,Markus,Bengt&KnutAkesson6havepracticeddiscreteeventsystems.TheyhaveusedEFA(ExtendedFiniteAutomata)asmodelingtoolandfindingfaults,particularlythisworkcanbeseenmorefocusedonextensionoffiniteautomataandmodeling.Similarly,intheworkofPLCdiagnosis,Z.D.Zhou,Y.P.Chen,J.Y.H.Fuh,&A.Y.C.Neehaveapproacheddistinctmethodology,whichcombinesbothhardwareandsoftware.Theyhavepresentedworkstructurallyusinghybridstrategywithmultiplesensorsandmulti-associatedparametersinthesystem7.However,theirworkcanbeseenasinclinedtohardwareimplementationtoavoidfaults.SomenotablyadvanceworkshasbeencarriedoutinPLCmonitoringbyHaoZhang,JianfengLu,YunjunMu,ShuogongZhang,LiangweiJiang8.Intheirpaper,onlinemonitoringofPLChasbeenillustrated.TheyhavedevelopedBPMS(Bao-steelPLCMonitoringSystem)applicationforthemonitoringwhichrunsonPC.Although,theirworkstressesondevelopmentofPLCmonitoringsystem,detaildescriptionofmechanismisnotexplained.Recently,theuseofneuralnetworkinthediagnosisofPLCcanbeseeninthepaperofMagdyM.Abdelhmeed,HoushangDarabi9.Particularly,theyhaveappliedRNN(RecurrentNeuralNetwork),atypeofANNfordiagnosisanddebuggingofPLCprogram.Intheirwork,theyhaveproposedanalgorithmfortheconversionofLLD(atypeofPLCprogram).Thealgorithmwithtime-delayinhiddenlayersoutputshasbeenappliedtoconvertLLDintoaRNN;subsequently,theycarryoutfaultdetectionprocessontransformeddata.Althoughtheirworkonmonitoringisin-depth,howeverinrealscenariodiagnosisworkcanbecarriedoutwithouttransformingPLCprogramintoANN.Hence,theirworkcanbeconsideredredundant.Inaddition,theapplicationofRNNbecomescomplexandtakeshighcomputingtimerelativetootherAnns.MostofworksondiagnosisandfaultdetectionofPLCprogramseemtobefocusedonparticularside.Mostofthemethodologiesappliedareconcernedwithdiscreteeventsystem6,whereinrealsystemPLCinvolvescontinuousoranalogvalues.Someothersapplynewmethodshowevercomputingtimeandefficienciesareignored9.Toovercome,thesetwomajorlimitations,fullyconnectedfeed-forwardneuralnetworkcanbeappliedforthefaultdiagnosisandmonitoringofPLC-controlledmanufacturingline.Firstofall,diagnosisprocesstakesplaceindata-valueinwhichPLCprogramrelieson.Inotherhand,feed-forwardwithwidelyusedback-propagationlearningalgorithmisusedinthiswork,explainedinsection4.3.BackgroundWhenwetalkaboutfault-detectioninPLCprogram,weparticularlyfocusontolocatealterationsinthevalidPLCprogramsequence.ThesefaultsinPLCprogramcanbefoundcontinuousobservationsofPLCprogramvariables.Inthecontrollingofmanufacturingline,PLCaredeployedwhichareprogrammable.ThevalidPLCprogramisworkingprograminrealPLCdevicewhichallowsmachinestobehavenormally,asperinstructionsgiven.BecauseofdifferentprocessparameterssuchassensorinputsthereisalwayschanceofbeingmodificationinoriginalvalidPLCprogramsequence.Inotherway,theobjectiveofmonitoringbecomesfindingerrorsoralterationsinprogramsequence.Whentherearealterationsinprogramsequencei.e.itdoesntmatchwithoriginalvalidprogramsequence,refersthatthereexistsfault.Inourwork,weadoptneuralnetworkformonitoringpurposeassuitablemethod,withappropriatelearningalgorithm,back-propagation.DeterminingthenetworkarchitectureisoneofthemostimportantanddifficulttasksinthedevelopmentofANNmodels.Ingreatextent,theefficiencyofANNdependsuponarchitecturemodeler,sincetherearesomejudgmentalfactorswhichhavetobedecidedondesigntimeofnetwork.Itrequirestheselectionofthenumberofhiddenlayersandthenumberofnodesineachofthese.Ithasbeenshownthatanetworkwithtwolayers,wherethehiddenlayerissigmoidandtheoutputlayerislinear,canbetrainedtoapproximateanyfunctionprovidedthatsufficientconnectionweightsareused10,11.Consequently,weuseonehiddenlayerinthiswork.Thenumberofnodesintheinputandoutputlayersarerestrictedbythenumberofmodelinputsandoutputs.TheinputlayeroftheANNmodeldevelopedinthisworkhastwonodes,oneforrelayinputandPLCprograminputi.e.binaryvalue.Similarly,theoutputlayerhastwonodesforvalidtwobits,0&1.Thephysicalinterpretationoftheconnectionweightsisimportant;hencethesmallestnetworkthatisabletomapthedesiredrelationshipshouldbeused.Consequently,themodelthathastheoptimumnumberofnodesgivinganoptimumgeneralizationisretrainedanumberoftimeswithdifferentweightsandbiasesuntilnofurtherimprovementoccurs.ThemodelarchitectureisshownbelowinFig.1.Fig.1Two-layerfeedforwardNeuralNetwork(fullyconnected4.MethodologyThestepsfordevelopingANNmodels,asoutlinedbyMaierandDandy12,areusedasguideinthiswork.Theseincludethedeterminationofmodelinputsandoutputs,divisionandpreprocessingoftheavailabledata,thedeterminationofappropriatenetworkarchitecture,optimizationoftheconnectiontrainingweights,stoppingcriteria,andmodelvalidation.MATLABisusedtosimulateANNoperationinthiswork.Thetrainingmethodusedinthisworkback-propagationalgorithm,isconsideredageneralizationofthedeltarulefornonlinearactivationfunctionsandmultilayernetworks.Thismethodiswidelyusedsupervisedlearningmethodbecauseofitsweighterrorcorrectrules,canbeillustratedasfollows:Thispredictionmodelcouldbedesignedasfollows.Istheestimatedoutput,andisthecorrespondingresidual,Theback-propagationtrainingalgorithmisaniterativegradientdesignedtominimizethemeansquareerrorbetweentheactualoutputofmulti-layerfeedforwardperceptionandthedesiredoutput.Itrequirescontinuousdifferentiablenon-linearity.Thefollowingassumesasigmoidlogisticnonlinearity.Step1:Initializeweightsandoffsetsallweightsandnodeoffsetstosmallrandomvalues.Step2:PresentinputanddesiredoutputsPresentacontinuousvaluedinputvectorX0,X1.XN-1andspecifythedesiredoutputd0,d1,.dM-1.Ifthenetisusedasaclassifierthemalldesiredoutputsaretypicallysettozeroexceptforthatcorrespondingtotheclasstheinputisfrom.Thatdesiredoutputis1.Theinputcouldbenewoneachtrialorsamplesfromatrainingsetcouldbepresentedcyclicallyuntilstabilize.Step3:CalculateActualOutputUsethesigmoidnonlinearityfromaboveandformulasasinfig3tocalculateoutputy0,y1.yM-1.Step4:AdaptweightsUsearecursivealgorithmstartingattheoutputnodesandworkingbacktothefirsthiddenlayer.AdjustweightsbyInthisequationw(t)istheweightfromhiddennodeiorfromaninputtonodeijjattimet,w,iseithertheoutputofnodeiorisaninput,isagainterm,and,jjisanerrortermfornodej,ifnodejisanoutputnode,thenWhereisthedesiredoutputofnodejandistheactualoutput.Ifnodejisjdjyaninternalhiddennode,thenWherekisoverallnodesinthelayersabovenodej.Internalnodethresholdsareadaptedinasimilarmannerbyassumingtheyareconnectionweightsonlinksfromauxiliaryconstant-valuedinputs.ConvergenceissometimesfasterifamomentumtermisaddedandweightchangearesmoothedbyStep5:Repeatbygoingtostep25.Result&DiscussionTheprocessofoptimizingtheconnectionweightsisknownastrainingorlearning.Thisisequivalenttotheparameterestimationphaseinconventionalstatisticalmodels.Theaimistofindaglobalsolutiontowhatistypicallyahighlynonlinearoptimizationproblem.Asinourwork,wedonotconsidertimeparameterso,feed-forwardnetworkisused,ratherthanrecurrent.Themethodmostcommonlyusedforfindingtheoptimumweightcombinationforfeed-forwardneuralnetworksistheback-propagationalgorithm,whichisbasedonfirst-ordergradientdescent.Inthisstudy,thegeneralstrategyadoptedforfindingtheoptimalparametersthatcontrolthetrainingprocessisasfollows.Foreachtrialnumberofhiddenlayernodes,randominitialweightsandbiasesaregenerated.Thenetworkistrainedusingthetrainingdatasetandvalidatedwiththevalidationdataset.Alltheillustrativediagramsaredescribedasfollows:Fig2PlottedRawdataFig3Transformation(digitized)Fig4ResultofSimulationInfigure2,therawdatafromrelaysisplotted,strengthofsignalbeingobservedinverticallyandtimeinhorizontalaxis.Here,transformationofrelayinputvaluesintodiscreteisourfirstobjective,whichisaccomplished.Thosevaluesaretransformedintodiscreteorbinaryvalueinfigure3usingthresholdvaluesdescribedinsection4.Infigure4,simulationresultcanbeseenwhereasundesirable,predictedusingapplying2hiddenlayers.Incomparisontofigure4,thepredictionisperfectinfigure5,whichisproducedusingonlyonehiddenlayer.Inthisway,morehiddenlayerscannotbeconsideredalwayssuperiortolesshiddenlayers,alsocanbeseeninfig6.Table1.Settingparameteroffeed-forwardnetworkFig6SimulationresultFig7using1hiddenlayerlearningratesFig8using2hiddenlayerslearningratesIntheabovefigure(7,8),thenumberofiterationismappedhorizontallyanderrorrateasvertically.Fromthefigure,higherthenumberofiterationslowerwillbeerrorrate.Theerrorinfigure4islessthaninfigure3.Figure4isconsideredtobesuitable;in16iterationsithasgoterrorratelessthan0.005.Inaddition,thesettingourfeed-forwardneuralnetworksettingisdescribedinthetable1.Table2.Settingparameteroffeed-forwardneuralnetworkforsequencepredictionFig9PLCprogrammingsequencedataFig10EventofPLCprogrammingpredictionIntheabovefigure7isPLClogicsequenceandfigure8istopredictthesequence.Itisusefulforthelogicsequenceoperationerror.Theerrorislessthan0.05.Inaddition,thesettingourfeed-forwardneuralnetworksettingisdescribedinthetable2.6.ConclusionMonitoringofPLCcontrolledfactoryfloorinvolvesobservationofPLCprogram.PLC-controlprogrambeinglowerlevelandinflexibleintermsofprogramming,itbecomesdifficulttodebugandtroubleshoot.Todetectfaultsandabnormality,robustmethodsarerequiredforprediction.Neuralnetworkisfoundtobesuitablemethodbecauseofitsinherentnature,parallelprocessingandpredictive.Inthisresearch,usefulresulthasbeenachievedFig4,intransformationofintegervalueintodigital.Importantly,predictionratehasbeenachieved99.995%.Thismethod,inthefuture,maybefurtherappliedtoothermanufacturingprocessestoo.中文译文基于神经网络的逻辑故障监测的PLC控制系统摘要本论文综述了用PLC程序实现故障的检测。在PLC设备控制的生产线中,PLC控制的程序是底层组件，它成为一种控制机制，这是生产线的自动化所依赖的。在现代的制造业中，PLC设备可以智能的控制整个生产线。换句话说，PLC程序可以管理整个过程结构的程序。我们目前对它们进行系统监视是PLC程序检测故障的一种方法。在运行过程中可编程序控制器(PLC)装置，可以严格监视PLC设备的操作或PLC程序的执行过程中的传感器信号，随后，控制程序去分析诊断信号。这样一来PLC程序可以全面监测到从时间上可能出现的错误参数(例如电压、偏等)。此外,可能改变程序和违规行为的可以性达到最小。这样可轻松地使用故障检测、方便维修和决策来控制生产过程。同样，它可以减少机器停机时间，并防止可能出现的风险。关键词：故障检测，PLC，集中控制系统1.简介在现代制造中，采用到自动化的任务中是编程控制器(PLC)的一个应用范围。一般工业领域，会按预期对生产系统进行维护和检修，该功能成本比自动化的成本相对要高。可编程序控制器(PLC)是专为工业环境设计的，其目的是形成多个输入和输出，延长温度范围、抗电噪声、振动和冲击。可编程序控制器(PLC)控制日益普及的原因是控制的灵活性，制造控制通过PLC程序执行可能的变化，PLC程序确定制造工业的自动化水平。换句话说，对整个过程结构进行建模和生产线控制，最终形成了可编程序控制器(PLC)。这样一来PLC程序将成为现代制造业的基础组件。然而，由于PLC编程系统相对于高水平的语言能力，他们在故障检测与诊断能力是有限的。PLC程序的连续的监测是为了减少机器停机时间，对机器安全性至关重要，对防范潜在的风险也是至为重要的。由于过程中涉及的数据特性：模拟和离散，PLC程序诊断变得困难。PLC设备执行连续扫描程序，并操作所涉及的机器发送I/O离散或数字的信号。但是压力、温度、流量及重量通常是用整数值表示的。因此，在二进制或整数表示输入和输出信号值时，总会在生产线运行中有大小的变化，那是因为PLC程序在输入和输出值可能表现的异常的变化所引起的。但值得注意的是许多成功的故障诊断的系统应用程序的使用并不常见，人工系统是一种人工智能技术，而他们的体系结构的方式是尝试模拟人脑和神经系统的生物结构。人工神经元的概念起源于1947年，虽然到应用程序中的人工系统研究的发展传播的算法始于1986年。人工系统因此可以考虑一个相对较新的工具领域的预测。前馈系统用于预测和预报，与建模哲学是相似的，更多的常规统计模型使用还在开发当中。这两种情况均在该模型中的目的是捕获模型输入，一个输入和相应的输出之间的关系。这被通过反复反馈到模型的输入/输出关系的示例和模型系数调整中，由输出和由模型预测，输出一个错误函数最小化的尝试。虽然一些系统模型不明显不同，数量的标准统计模型它们极有价值，他们属于类的数据驱动的方法，而传统的统计方法是驱动模型。在我们的工作中，我们设计了一种系统传播的学习方法。系统程序是用来预测PLC程序有效的输入和输出(I/O)的控制程序。最后，对系统性能预测工艺参数进行了研究。2.文献回顾越来越多的研究者和工业合作伙伴都被吸引到这个区域，用于故障检测和监控系统。到目前为止，有关的诊断方法也得到了发展。胡.施罗德，A.G.舍提出了以知识为基础的实时可编程序控制器(PLC)诊断系统。他们的工作是集中在获取知识的气动、液压回路图和PLC程序。简单地说，找回可编程序控制器(PLC)数据并找出可能的缺点。在这个过程的观察和故障检测中，托德Alenjung马库斯，本特.可努特，埃克森进行了离散事件系统的实验。也已经得到了使用(扩展有限自动机建模工具)，用来发现错误，尤其是该产品可以看到更多的、集中的有限自动机建模与扩展中。同样在工作中，可编程序控制器(PLC)诊断中，Z.D.周，Y.P.陈，J.Y.富，和A.Y.C.Nee研究过结合硬件和软件的不同方法，他们提出了在正常工作中在结构上，使用具有多个传感器和工作相关的参数混合策略系统。然而，为避免错误，他们的工作可以被看作是倾向于硬件实现的电路，一些著名的作品已被预先通过PLC进行监测。张浩、陆剑峰、亩云军、张所供、江梁伟在论文中，对在线监测的可编程序控制器(PLC)也已经做了相应的说明。他们已经开发了BPMS(宝钢PLC监控系统)的监测，以监察其PC机上运行的应用程序，但是他们的工作重点是对PLC监控系统的开发，而不详细描述介绍机制。最近，在MandyAbdelhmeed，Houshang.Darabi的文章中可以看到的系统在PLC的诊断中得到使用。尤其是，他们应用了RNN（递归系统）的人工系统诊断和调试的PLC程序的类型。他们在工作使用LLD(一种PLC程序）的转换的算法，提出了一种转换LLD(一种PLC程序)的方法。实质其中隐藏图层输出算法已应用于将LLD转换为一个RNN，随后，他们进行故障检测已转换的数据处理。虽然深入开展监测的工作，但在实际情况下诊断工作情况，但不变换到PLC程序到ANN。因此,他们的工作可以被认为是多余的。此外RNN相对于其他其他人工系统的应用程序来说变得复杂和需要较多的计算时间。PLC程序诊断及故障检测的工程大部分似乎集中特定方面。应用该方法的大多数都关心离散事件系统，而在实际的系统中，PLC涉及连续或模拟的值。有些应用新的方法，但是计算时间和效率是忽略。要克服这两个的主要限制，完全连接的前馈式系统可以应用PLC控制故障诊断与监测生产线。首先，诊断过程发生在PLC程序依赖于的数据值。在另一方面,基于前馈反向传播与广泛使用的学习算法适用于这项工作，解释了在第四节。3.背景当我们谈到在PLC程序中的故障检测时，我们特别着重查找有效的PLC程序序列中的变化。这些错误在PLC程序中可以连续观测到PLC程序变量。在控制生产线的可编程控制器是可以进行编程的。有效的PLC程序是让真实的PLC设备根据指令使机器正常工作。因为不同的工艺参数，如传感器输入总是有机会被PLC程序序列修改原始有效期。其他的方式的监测目标为发现错误或更改程序序列。有程序序列变化时即它不会与匹配原始的有效程序序列，是指存在故障。在我们的工作中，我们采用控制系统合适的方法是进行监测为目的，并给出了合适的学习算法。确定网络体系结构是发展的系统模型中最重要、最困难任务之一。在很大的程度上人工系统的效率取决体系结构建模者，因为有一些判断的因素是要决定于网络的设计时间。要求的数目选择隐藏层，在每个节点的数