3.Processstatemonitoring

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1 Chapter14 Processmonitoringandvisualisation O.SimulaandJ.Kangas HelsinkiUniversityofTechnology,LaboratoryofComputerandInformation usingself-organizingmaps Science,Rakentajanaukio2C,02150Espoo,Finland,Fax:358(0) , 1.Overview algorithmcanbeusedtoinvestigatecomplexdependenciesbetweenvariousprocess possibletopredictallpossibleerrortypesinadvance.theself-organizingmap problemarea.incomplicatedsystems,asinchemicalprocesses,itisnot systemstomonitorcomplicated,dynamicalprocessesandtovisualisetheprocess development. parametersaswellasinputandoutputvariables.itcanalsobeutilisedtocreate Analysisandcontrolofcomplexnonlinearprocessesconstitutesadicult algorithmisthetopologicalnatureofthemapping;similarsignalpatternsare inputpatternstomapunits.thekeypointintheapplicabilityofthesom sequenceanalysisbyndingthemappinglocationsofsubsequentpatternsand 2.Introduction mappedtonearbylocationsonthemap.suchamappingcanbeappliedtopattern observingthetrajectory,thatisthecurveofthelocationsintime.thepattern applications. twodimensionaltrajectorywhichcanbeused,forexample,inprocessmonitoring sequenceinmulti-dimensionalinputspacecaninthatwaybetransformedintoa Theself-organizingmap(SOM)algorithm[8][9]createsamappingfrom monitoringisdescribed.someapplicationsinvisualisationoftheprocessoperation Inthefollowing,theuseoftheself-organizingmapinprocessstate

2 analysistaskstoprovidethevisualisationofcertainsignalcharacteristics. 372 arepresented.inadditiontovisualmonitoring,thesomalgorithmcanbeapplied infaultdetectionandanalysis.examplesoffaultdiagnosisofdevicesandprocesses ApplicationswheretheSOMalgorithmhasbeenusedinvisualisationofspeech signalsandspeechsignalvariationsintimearealsodescribed.finally,other applicationsofthesomalgorithminmonitoringandsignalanalysisareshortly covered.moredetailedreviewoftheuseandapplicationofthesomalgorithm willbefoundin[6]. Theself-organizingmapalgorithmcanalsobeappliedtoothersignal 3.Processstatemonitoring Inpracticalapplications,alsotheonlineprocesscontrolbasedonthestateanalysis anecientandunderstandableway.theonlineprocessstatusmeasurements visualisethetypicallycomplexrelationsbetweenvarioussystemparametersin shouldbeconvertedtosomesimpledisplaysthat,despitethedimensionality processmonitoringisoftencarriedoutinordertoestimatethefuturebehaviour. Reliablepredictionofthefuturebehaviourcouldresultinecientfaultdiagnosis. reduction,preservetherelationshipsbetweenstates.simultaneously,itshould bepossiblefortheusertofollowtheprocessstatedevelopment.furthermore, Inprocessstatemonitoringapplications,theproblemistoanalyseand spacetotwodimensionalsurfaceoftheprocessingunits.themappingis, furthermore,doneinsuchawaythatthetopologicalrelationsbetweentheinput isanimportantaspect. featurevectorsarepreserved.inmonitoringapplications,theself-organizingmap processparameters. algorithmcaneasilybeusedtoanalysethecomplexrelationsbetweenthevarious Theself-organizingmapmethodmakesamappingfromamultidimensional preservingpropertyofthemap,similarfeaturescorrespondingtosimilarstatesof theprocessaremappedclosetoeachotherresultinginclustersonthemap. variablesordata,(2)theprocessparameters,and(3)outputsoftheprocess.all theseparameterscanbeconcatenatedtoformafeaturevectorwhichisusedas aninputtotheself-organizingmap,asshowninfigure1.duetothetopology threetypesofvariablesorprocessparametersthatmustbeconsidered:(1)input Themappingiscreatedinanunsupervisedwayfromthemeasureddata Incomplexprocesses,e.g.inchemicalengineering,thereareusually theprocess.thisisdepictedinthelowerpartoffigure1.onlyalimitednumber eachclusteronthelabeledmapcorrespondstoacertainstateoroperationpointof thislearningphase.thephysicalinterpretationofthemapcanbeobtainedby labelingthenodesofthemapaccordingtotheknownprocessbehaviour.the nodescorrespondingtosimilarfeaturesaremergedinthelabelingprocess.thus, andparameters,i.e.noknowledgeoftheprocessbehaviourisrequiredduring

3 Measurement vector (Feature vector) 373 Input measurements Output measurements Process Material Material flow in flow out ofpreclassiedsamplesarerequiredinthelabelingphase.usingthelabeledmap Process parameters inmonitoring,wecannowidentifythestateoftheprocesscorrespondingtoa certainoperationpoint. Figure1.Featurevectorobtainedfromprocessparametersanddata (above).physicalinterpretationoftheself-organizingmapbylabeling A C 3.1.Visualization theclustersofsimilarfeaturestocorrespondingprocessstates(below). E B Map training D and labeling behaviour[7].theparameterofinterestcanbeextractedfromthefeaturevector Theself-organizingmapisanecienttoolforvisualisingtheprocess Self-Organizing Map infigure2. bydisplayingitsvalueasagraylevelonthemap.followingthetrajectoryofthe operationpoint,wecaneasilymonitortheparametervalue.anexampleisshown oneofthetemperatures.darkgraytonescorrespondtolowandlighttonestohigh analysedduringaperiodof24hours.thetrajectoryisdrawnonthemapdisplaying Thetrajectoriesofthesuccessivedayswereverysimilarduringnormaloperation. temperatures,respectively.itcanbeseenthattheoperationpointhasmovedfrom darktolightandbackagaincorrespondingtothedaytimeoperationofthesystem. Inthisexample,measurementsoftheprocessparametershavebeen

4 parameterdependenciesandtheireectonprocessbehaviourcaneasilybe 374Simulateddataandprocessparameterscanalsobeusedinthelearning investigated.forinstance,theeectofcertainprocessparametersonprocess phase.theself-organizingmapcan,thus,beusedinprocessmodeling.various stateoroutputcanbevisualisedonthemap. InFigure3,eightdierentparametersofinteresthavebeenextractedfromthe featurevector.theparametervaluesandtheirdependenciescanbeanalyzed parametervaluescorrespondingtothestateoftheprocess. directlybycomparingthegraylevelsofcorrespondingmapunits.thetrajectory oftheoperatingpointcanalsobedisplayedoneachseparatemapgivingthusthe Figure2.Exampletrajectoryofaprocessstateduring24hours. Severalparameterscanbevisualisedsimultaneouslybyusingasetofmaps. vectoritispossible,forinstance,tomakeoverallcomparisonsofdierentprocesses. areoftencalculated.usingtheseguresofmeritasparametersinthefeature Theeectofvariousparameterstotheprocessbehaviourcanbeanalyzedandto someextenteven\optimised".asanexample,thesomalgorithmiscurrently beingappliedintheanalysisofapulpprocess,whereespeciallytheairandwater emissionsoftheprocessareofinterest. Inevaluatingthequalityofcomplexprocesses,variouscharacteristicgures 3.2.Faultdiagnosis toidentifythefault.inpracticalapplications,wecandistinguishbetweentwo diagnosis.themapcanbeusedintwoways:(1)todetectthefaultand(2) Anotherimportantapplicationoftheself-organizingmapisinfault

5 375 (a) (b) (c) (d) dierentsituations;eitherwehavenopriormeasurementsofthefaultysituations Figure3.Asetofmapscorrespondingtodierentparameters. Therefore,onlythesituationsincludedintothetrainingdatacanberecognisedby thelabeledmap. orwehavebeenabletorecordalsofaults. situations,theoperationspaceonthemapcoversonlynormalsituations.fault processthatarecoveredbythemeasurements.thus,thestatespacewillbedivided intotwoparts:(1)thepossibleoperationspaceand(2)itscomplementaryspace. Incasethetrainingdatahascontainednomeasurementsfromfaulty Inthelearningphase,themapistrainedtorecogniseonlythosestatesofthe (e) (f) (g) (h) faultysituation.thisconclusionisbasedontheassumptionthatduetothelarge distanceofthefeaturevectorfromallthemapnodestheoperationpointmust mapunits.ifthedierenceexceedsapredeterminedthresholdtheprocessisina correspondingtothemeasurementiscomparedtotheweightvectorsofallthe belongtothecomplementaryspace,notcoveredbythetrainingdata.therefore, detectioncannowbebasedonthesocalledquantisationerror.thefeaturevector thesituationhasnotoccurredbeforeandsomethingispossiblygoingwrong. situations.inthiscase,clusterscorrespondingtocertainfaultsarecreatedon themapandtheseclusterscanbeconsideredas\forbidden"areas.thefaultcan nowbeeasilyidentiedbyfollowingthetrajectoryoftheoperatingpoint.ifthe mapincludingoneforbiddenareaisshowninfigure4.inthisparticularexample, trajectorymovestoaforbiddenareathefaultwillbeidentied.anexampleofthe Infaultidentication,thetrainingdatamustcontainsamplesoffaulty

6 376 thetrajectorypassestheforbiddenareaandthefaultcorrespondingtotoohigh temperatureincertainpartoftheprocessisidentied. correspondingtovariousfaultscanbecreatedonthemap.thisispossibleif canbeproduced.thisisespeciallyimportantinsituationswherefaultsarerare faultysituationsandtheirreasonsareknownwellenoughsothatsimulateddata andtruemeasurementsarethusnotavailable. Figure4.Forbiddenareaonthemapcorrespondingtoafaultysituation. 3.3.Faultdetectionandidenticationsystem Infaultanalysis,simulateddatacanalsobeused.Forbiddenareas anaesthesiasystemisdescribed.theanaesthesiasystemcomprisestheanaesthesia machine,thepatient,andtheanaesthesiapersonnel.thepurposeofmonitoringis tominimisetherisksofanaestheisiabydetectingandidentifyingthefaultsbefore theycauseinjurytothepatient[22],[23]. Inthissection,anexampleofthefaultdetectionandidenticationinan reasonwillbeidentied.thefaultdetectionandidenticationsystemisdepicted inanalysingfaultsoralarms,asshowninthelowerpartoffigure5.ontherst usingthesocalledfault-detectionmap.onlyafterthedetectionofthefault,its indetailinfigure5. notpossibletoexactlydene\normal"situations,twolevelsofmapscanbeused thefaultisdetectedbasedontheincreaseinthequantizationerror.thisisdone Thedetectionandidenticationoffaultscanbedoneintwostages.First, Indynamicalsystemswheretheoperationpointisnotstableorwhereitis

7 measurements (feature vector) fault detection map visualization of quantization error 377 Figure5.Faultdetection(upperpart)andidentication(lowerpart)system. fault registration of ring buffer operation point visualization of operationspaceintodierentparts.theamountofdeviationcausedbyafaulty beusedtolocatefaultsbasedonthedeviationsintheparameterscomparedtothe operatingpoint. trajectory situationmaystronglydependontheoperationpointofthesystem.thus,each measurements.onthesecondlevel,amoredetailedmap(orasetofmaps)can level,theself-organizingmapisusedtoidentifytheoperatingpointbasedonthe 1st oftherstlevelmap.bystoringasequenceoffeaturevectors,thebehaviourof theprocessbeforeandduringtheoccurrenceofthefaultcanbeanalysedinmore maponthesecondlevelcorrespondstoarelativelysmallpartoftheoperationspace Thepurposeoftherstlevelmapistoincreaseaccuracybydividingthe 2nd maps detail.themonitoringsystemwasimplementedbycollectingtrainingdataina obstructionsinthetubesindierentpartsoftheanaestehesiasystem.inthe realoperatingroomenvironment.dierentfaultconditionsincludedleaksand experiments,therecognitionaccuracyofthesecondlevelmapwas87%onthe oftime.figure6(a)showsthequantizationerrorofthefaultdetectionmap.in samplesfromtruesituationsaswell.inthisexample,thepositionofthepatient considerablefromthoseofthetrainingset,therecognitionaccuracydecreasedto 70%ontheaverage. pointsofthetrainingset.whenthelocationoftheoperationpointdeviated waschangedandtheintubationtubewasaccidentallyobstructedforashortperiod average,iftheoperationpointofthetestsetwasrelativelyclosetotheoperating Figure6(b)itcanbeseenthatthetrajectoryoftheoperationpointmovesfrom Theperformanceofthefaultdetectionandidenticationwastestedusing

8 378 theareacorrespondingtonormalsituation(n)toobstructionarea(o5). Figure6.Quantizationerrorofthefaultdetectionmap(a)andthetrajectoryof mapalgorithminanalysisoftimeseriesdata.wehaveusedthespeechsignalasthe 4.Visualizationofspeechsignals Thefollowingexampledemonstratestheapplicationoftheself-organizing theoperationpointduringthefaultysituation(b). inputdata,butsimilardynamicphenomenacanbedetectedinmanyprocessesand theanalysismethodsarerathergeneric.studingthesimilaritiesanddierences alsotakesintoaccounttheprobabilitydensityfunctionoftheinputsamplesso preservesthesimilarityoftheacousticsignals(toacertaindegree).themapping soundsaremappedtonearbylocationsonthemap(plane).themappingthus ofanydynamicprocess. betweenspeechsamplesisanalogoustocorrespondingstudiesforoperatingstates thatthedistributionofinputstothemapunitsapproximatesthedensityfunction amappingfromthespeechsignalspacetoatwodimensionalplane.similarspeech directly.perceptuallymeaningfulfeaturesofspeechderivefromcomplexfeatures Inspeechanalysistheself-organizingmapalgorithmhasbeenusedtocreate thereforeeasilycomprehendablewhencomparedwith,forexample,spectrograms. Becausetheprobabilitydensityfunctionoftheinputsamplesisalsotakeninto locationsonatwodimensionalmap.visualizationofvoicequalitywithsomis ofmorecommonsounds.itispossibletodirectlyobservethesimilaritiesofthe inthespeechspectra.suchfeaturesarerepresentedbyself-organizingmapas account,thosesoundsthataremorecommonarerepresentedwithmoredetails thanlessfrequentlyoccurringsounds.thusmoreemphasisisputonthedeviations Quantization error Intubation tube obstructed (O5) 0.0 t (a) (b)

9 soundsbyobservingthedistancebetweentherespectiverepresentationsonthe map. 379 from15men.themapareasforthevowels,/s/and/r/areshownon Figure7.Aself-organizedmapcomputedwithFinnishspeechsamples somedysphonicpatientsaredepictedbythetrajectorycurve.someexamplesof timespectraformatrajectoryonthemapandchangesintimecanbeobserved thechangingofspeechspectraintime.therepresentationsofconsecutiveshort- fromtherepresentations.theabruptchangesinthespeechsignalobservedin bythelinebeginningfrom/s/areaandendingin/i/area. themap.thetrajectoryproducedbyanutteranceof/sa:ri/isindicated speechtrajectoriesonself-organizedmapsareshowninfigures7and8. Theself-organizingmapmethodisespeciallyusefulforthevisualisationof Figure8.Aself-organizedmapcomputedwithFinnishspeechsamplesof18 ThemapsinFigures7and8wereusedtoanalyzethevoiceduringlong women. u i e u o o y i e a r ö x a y x ö ä r ä male map female map s s

10 ofthesamples,thefollowingparameterswerecalculated. computingdiagnosticguresfromthetrajectory.fortheevaluationofthestability 380 /a:/vowels.wemeasuredthesmoothnessandregularityofthespeechsoundby 2.themeanlengthofshifts,and 3.thenumberofcaseswhereconsecutivesampleshavethesamerepresentations 1.Thetotallengthofthetrajectoryduringanintervalof150ms, issmoothwithoutanyabrupt`jumps'initbutitislocatedona`wrong'areaon variations)canalsobeobservedfromthetrajectory.inthesecases,thetrajectory andthenumberofconsecutivesampleshavingthesamerepresentationsgivesan indicationofasmallscalestability. Thelengthoftrajectoryindicateslargescalechangesinconsecutivesamples, onthemap. themap.whennormalspeakersutteraknownphoneme,weexpectthesamplesto beprojectedintoaspecicarea.normalinterspeakerdierencesareseenassmall dierencesinlocationswithinthisarea.bydeterminingasampletrajectoryin referencetothis`normalarea',wecanthusdiagnosethe`normality'ofthespeech sound. Thepermanentchangesinthespeechspectra(asopposedtocycle-to-cycle 4.1.Experiments experiments. wasdescribed.inthispaper,theself-organizingmapwasusedtovisualisethe speechsignalandthespeechsignalvariationsintime,andfromthemapping createdbythesomprocessitwasfurtherpossibletoconstructsomequantitative Theaboveideasonspeechsignalvisualisationhavebeentriedinseveral speechsignalanalysisisdescribed.inthispapertheself-organizedmap analyzed. measuresofthevoicequality.inthestudiesthevoicequalityofvowelswas In[11]anapproachtodevelopanautomaticdeviceforvoicequalityanalysis wasusedtodistinguishbetweenthe/s/samplesperceptuallyclassiedas themapwasasuitabletoolfortheextractionandmeasurementofacousticfeatures acceptable/unacceptable,asjudgedby21speechpathologists.itwasshownthat thevowelfollowingaword-initial/s/clearlyaectsthespectratowardstheend correspondingtotheaudibledeviationsof/s/. In[15]anotherapplicationoftheself-organizingmapalgorithmfor changesinspectraduetothepreparatorymovementsoflipsandtongueforthe ofthe/s/sound.thusthefollowingvoweltypecouldberecognisedbasedonthe followingvowel. Fromacousticvoiceanalysisusingspectrogramsitcanbeobservedthat

11 organizingmapalgorithmcouldextractuseful,perceptuallymeaningfulfeaturesin the/s/sound.theobjectivewastoseeiftherepresentationsofthe/s/samples beforedierentvowelscouldconsistentlyberecognisedfromthetrajectories producedbyprojectingtheconsecutivespeechsamplesintoamapplane. In[12]and[13]thephenomenonwasfurtherstudiedtondoutiftheself OtherApplications processmonitoringwasdescribed.self-organizingmapwasusedtodetectabnormal statesinareal-timeprocessbyexaminingthequantisationerrorbetweenthebest thebenetsofthealgorithmasappliedtomonitoringapplicationswerepointed usingneuralnetworksinfaultdiagnosisofachemicalprocesswasexplained. out.theexampleapplicationwasadistillationprocess.in[17]anotherstudyof andcontrolwasexplained.thesommethodwasexplainedinsucientdetailand In[1]apreliminarystudyofthepotentialoftheSOMalgorithmfor In[18]theapplicabilityoftheSOMalgorithmtoprocessstatemonitoring thatwereencounteredduringthetraining.risingquantisationerrorobviously determinedusingaknownsetoferrorsamples.asimilarsystemwasdescribedin indicatedsomeprocessstatethathadseldomoccurredduringthetrainingperiod. Asimilarprincipleoferrordetectionwasusedalsoin[7]wheresomeerrorstates wereclassiedbyobservingtheprojectionofsamplesto`forbidden'areasintomap, matchingunitonthemapandtheinputvector.thedetectionwasbasedonthefact [4],wherethetrainingalgorithmswereimproved. thatthemapunitsweredistributedinthespaceoccupiedbythoseinputsamples powerplant,whereitisimportanttonoticepreviouslyunknownsituations.the ofanenginecondition.theresultswerepromising.theauthorsfoundthesom quantisationerrorgivenbythesomalgorithmisusedtogivesomeindicationof tobeespeciallyusefulinthevisualisationofdatapropertiesandhighlightingthe thenoveltyoftheinputsample. autoassociativeback-propagationmodel)wereappliedtoamonitoringapplication In[2]asimilarsystemasabovewasusedtodetecterrorsinanuclear deviantdatavalues.in[21]au-matrixmethodforthevisualisationoftheprocess propertiesthroughthemapwasexplained(theu-matrixmethodwasintroduced in[19]and[20]). In[3]twoneuralnetworkmodels(self-organizingmapandakindof anexampleonecantakethepaper[16],whereself-organizingmapwasusedfor fordierentkindsoffailuredetection.in[5]severalstudieswerereviewed.as fromagroupofspanishbanks.itwasshownhowdierentregionsonthemap ofsuccessiveyears. representedsolventandbankruptbanks,respectively.itwasalsopossibletofollow thetimeevolutionofthebanksfromthetrajectorycreatedbymappingthedata In[14]theself-organizingmapwasusedtoanalyzesomenancialdata Theself-organizingmaphasbeenusedtomonitorelectricpowersystems

12 382 powersystemstaticsecurityassessment.itwasshownthatthemapcanbeused collectedfromtestcarduringtestruns.thesystemconsistedoftwolayersofsoms wheretherstlayerhandledstaticmeasurementsandthesecondlayercollected inmonitoringofpowersystems. In[24]theself-organizingmapwasusedtovisualisemeasurementsignals REFERENCES datafromlongerdurationsoftime.thevisualisationofthemeasurementswas donebycolourcodingthemapunits;similardrivingstateshavethereforesimilar qualityofpaperfromprocessmeasurements.aseparateself-organizingmapwas colourcodes.thedrivingstatesduringthetestrunwasthenillustratedasacolour codedtrajectoryonamapoftestlane. usedtomonitorthemovementoftheoperatingpointoftheprocessandtogivea 6.Discussion hintoftheestimationerroroftheprimarynetwork. In[10]anerrorback-propagationmodelwasusedtoestimatethenal systems.duetothetopologypreservingpropertyofthesomalgorithmithas monitoringcomplexprocesses.thenonlinearmappingfromahighdimensional showntobeanextremelypowerfulvisualisationtool. inputspacetoausuallytwodimensionalgridecientlycharacterisescomplex Theself-organizingmaphasbeenappliedtovariousapplicationsin insimulatingandestimatingthebehaviouroftheprocess.variousparameterscan ofanysystemwheretruemeasurementsofprocessparametersorsimulateddata investigatedinastraightforwardmanner.themethodisapplicabletotheanalysis areavailable.forinstance,intheanalysisofcomplexchemicalprocessesvarious isnotasignicantdrawbackwithtoday'scomputers. dependenciescaneasilybeexamined.thehighdimensionalityofthefeaturevector Inadditiontomonitoringandvisualisation,theSOMalgorithmcanbeused UsingSOMthedependenciesofthesystemparametersandvariablescanbe beoptimisedbyfollowingtheprocessbehaviouronthemap.eventhecontrolof References complexsystemsmaybepossiblebyusingfeedbackfromthemonitoringsystem. [1]J.T.Alander,M.Frisk,L.Holmstrom,A.Hamalainen,andJ.Tuominen, [2]Y.Bartal,J.Lin,andR.E.Uhrig,Nuclearpowerplanttransientdiagnostics usinglvqorsomenetworksdon'tknowthattheydon'tknow,inproc. ICNN'94,Int.Conf.onNeuralNetworks,pages3744{3749,IEEE,Piscataway, Processerrordetectionusingself-organizingfeaturemaps,InT.Kohonen, K.Makisara,O.Simula,andJ.Kangas,editors,ArticialNeuralNetworks, USA,1994. pagesii{1229{1232,north-holland,1991.

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