Graph-based reasoning in collaborative knowledge management for industrial maintenance

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Kamsu Foguem, Bernard and Noyes, Daniel Graph-based reasoning in

collaborative knowledge management for industrial maintenance. (2013)

Computers in Industry, Vol. 64 . pp. 998-1013. ISSN 0166-3615

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Graph-based

reasoning

in

collaborative

knowledge

management

for

industrial

maintenance

Bernard

Kamsu-Foguem

*

,

Daniel

Noyes

LaboratoryofProductionEngineering(LGP),EA1905,ENIT,INPT,UniversityofToulouse,47Avenued’Azereix,BP1629,65016TarbesCedex,France

1. Introduction

Thecapitalizationof theexpertiseis nowa critical issuefor industrialbusinessesfacingthedemographicchangesinstaffing levelsandthevaluationproblemsofintangibleassets(business reputation,trainedworkforce,andnoncompetitionagreements). At the maintenance function, technological and organizational developmentsforceinternalandexternalcollaborationstoensure localandglobalperformanceofthatmaintainedsystem[1].We findthissituationinsocio-technical,networkedanddistributed systems(communication,power,transportation,etc.),for which the resources and maintenance skills are also very often distributed,withanincreasingrelianceontechnologicalnetwork systems[2].Otheremergingcollaborativenetworksaredeveloped byhumanprofessionalsthatmaycollaborateinvirtual communi-tiesandconstitutevirtualteamstodealwithspecificproblemsor concerns,andfindpathstobreakthroughsuccesses[3].Problems concerningtheexploitationofindividualpracticesaresometimes based on the management and the structuring of shared informationbyacollective[4].

Maintenance activitiesinvolve sooftencollaborative actions and decision-making in which groups of actors are working together through a common area of problem solving [5]. The

conceptualizationofthemechanismsinvolvedinthese collabora-tiveexchangesfacilitatestheintegrationofknowledge manage-mentincollaborativedecision-makingsystems[6].Theresearch on collaborative decision-making in maintenance management has highlighted the information needs for problem-solving methodsbutfewintegratetheaspectofknowledgemanagement [7].Toolsareavailableatthislevel[8,9]buttheyoftenrequirea suitable knowledge formalization so they can be shared in a context, which moved from reactive centralized processing architectures,todistributedintelligentinfrastructures.Moreover, thedeploymentofasustainableactionplanforriskmanagement requirestheintegrationofknow-howandcontextualized experi-encestodevelopandimplementappropriatemaintenancepolicies [10].Historically,ExperienceFeedback(EF)wasmainlybasedon statisticalmethods toidentifysomefailure laws.However,this kindoffeedbackdoesnotallowtheextractionofexpertknowledge fromthetechnicaldata.Thisismadepossiblebythe‘‘cognitive approach’’ofexperiencefeedbackmodeling.Itmodelstheexpert knowledgeoftheorganizationandfacilitatestheenrichment of knowledge repository by using methods from artificial intelli-gence. Intelligent data analysis and data mining support the extraction of patterns and regularities from the process data collectedduringmaintenanceactivities[11,12].The transforma-tionofsuchpatternsintoexplicitknowledgerequiresknowledge representationformalismsandtools.Thecognitivevision frame-workofexperience feedback provides meansofunderstanding, interpreting,storingandindexingtheactivitiesofexperts[13].EF appliedtothemaintenancemanagementtakesplaceascontinuous

Keywords:

Experiencefeedback Conceptualgraphrule Expertknowledge Maintenancemanagement Case-basedreasoning Fleetconsiderations

ABSTRACT

Capitalization and sharing of lessons learned playan essential role in managing theactivities of industrial systems. This is particularly the case for the maintenance management, especially for distributed systemsoftenassociatedwithcollaborative decision-makingsystems.Our contribution focusesontheformalizationoftheexpertknowledgerequiredformaintenanceactorsthatwilleasily engagesupporttoolstoaccomplishtheirmissionsincollaborativeframeworks.Todothis,weusethe conceptualgraphsformalismwiththeirreasoningoperationsforthecomparisonandintegrationof severalconceptualgraphrulescorrespondingtodifferentviewpointofexperts.Theproposedapproach isappliedtoacasestudyfocusingonthemaintenancemanagementofarotarymachinerysystem.

*Correspondingauthor.Tel.:+33624302337/562442718.

E-mailaddresses:bernard.kamsu-foguem@enit.fr(B.Kamsu-Foguem),

improvement that emphasizes the ongoing monitoring and verification of the root causes of problems in the monitored system to eliminate repetitive failures and recurring problems [14]. The approach proposed in this work is based on the EF exploitationincollaborativedecision-makingsituations.Thegoalis to improve collaboration among maintenance actors by the deployment of knowledge engineering toolstoeffectivelyshare experiences,generatingknowledgetobetterresolveproblems.The approachofdynamiccapitalizationofknowledgesupports knowl-edgevalidationprocessamongcollaboratingactors,hence increas-ingthereliabilityofcapitalizedknowledge[15].Problem solving willincludeknowledgereasoningbasedontheconceptualgraphs (CG)formalism[16].ThechoiceofknowledgerepresentationbyCG should enable a better understanding of critical situations and provideassistancetotheappropriatedecision-makinginorderto anticipate them [17]. The expected result is a better use of knowledge and skills distributed among different experts: (1) strengthening the collective knowledge with the promotion of accessbythecollaborativeactorstorelevantinformationandthe enhancementofplansforthemaintenanceofthetargetsystem,(2) facilitatingthesustainablemanagementofexperiencelearningand providingsupporttothemodelingandimprovementofthequality ofknowledgesharingwithinthecollaborativeorganization.

The paper is structured as followed. Section 2 exposes the proposed methodology and its main components concerning knowledgeengineering.Section3presentsthecognitive experi-ence feedback approach applied to industrial maintenance management.Section4presentstheconceptualgraphsoperations usedtoimplementthemodelingofexpertrulesincollaborative decision-makingprocesses.Anillustrativeapplicationexamplefor themaintenancemanagementofarotarymachinerysystemfrom the railway field is exposed in Section 5. Finally, Section 6 concludesanddiscussesfuturechallenges.

2. Collaborationinmaintenanceactivities:situationand requirements

2.1. Theneedsandtheactorsconcerned

Inthecontextofindustrialmaintenance,industrialoperationor industrial logistic support, simple or intelligence collaboration provides the opportunity for a new generation of maintenance systems. These systems are composed of self-ruling modules (increasingly moreintelligent) interconnectedby computer net-works.Asthenetworksrightlyemphasizes,thefunctioningofthe industrialmaintenanceisaffectedbythediversity,the heterogene-ityandsometimestheintensificationofinteractiveprocesseswith lessknowledgetransferofexpertise.Inordertoworkefficientlysuch interprofessionalcollaborationneedstobeaddressedbothinthe informationexchangeandtechnicaldataprocessing.Threatsrelated tothecontrolenvironmentincludeinformationsystems complexi-ty,timelyreviewofcollaborativeresultsandtherobustnessofthe principles of organization. Collaboration is needed in some maintenanceactivities(e.g.diagnostics,decision-makingat differ-entlevels)forseveralmotivations:

organizations and people are essentially self-governing, geo-graphically distributed, and heterogeneous in terms of their operatingenvironment,beliefsand communityofpracticesor competencies[18];

actors collaborate to (better) achieve communication and information exchange, complementarity or compatibility of goals, aligning activities, process of shared competencies to solveaproblemtogether;

open source/web-basedapplicationsarethe centralthreadof organization knowledge sharing (best practices) for efficient

decision-making of which the effective assessment of alter-nativesisacrucialelementofguaranteeingtherobustnessofthe maintenancepolicies.

These industrial motivationsintegrate existing maintenance principles with modern Web services and the principles of collaborativenetworks.Collaborationisalsousefulfor computa-tionalintelligencethatneedstobefluentlyre-configuredasguided bytherequirementsoftractabilityandagility.

Emergingcollaborativemaintenancenetworksareformedby humanresources (e.g.operator, sub-contractor, maintainer and experts) that maycollaboratein distributed organizationswith virtualprojectteams.Thecollaborativenetworksareagainformed by automated resources (e.g. Supervisory Control and Data Acquisition (SCADA), Computerized Maintenance Management System(CMMS),Health andUsageMonitoringSystem(HUMS)) andtheymayincorporateinformationtechnologymodulessuchas acquisition,processingandvisualization.Acollaborativeand user-friendlyCMMSisavailableontheindustrialapplicationsandwas developed for the technical departments mainly of production companies or companies who wish to plan the proactive maintenanceoftheircriticalequipmentandmachines.Moreover ‘‘automated collaboration’’ is advocated through ‘‘technical intelligence’’ collaboration implemented within HUMS, smart components(tosupportIntegratedVehicleHealthManagement (IVHM)conceptforexample)whereitispossibletofindmaster andslave.

AHUMSframeworkemploysnumeroussensorsconnectedtoa central data recording system, to collect quantitative data in embeddedsystem(e.g.flight,ship)fromsystemsandcomponents throughouttheembedded system.Thedata aredownloadedto ground-basedcomputersforfurtheranalysis(maintenance,cost, operationalandperformance).Typically,thedatarecordthetypes ofoperations theembedded systemhasperformed,and canbe usedtopredictthehealthandremaininglifeofcomponentswith streamlined logistics for fleet deployment. IVHM system for aerospace vehicles, is the process of assessing, preserving, and restoringsystemfunctionalityacrossflight andtechniqueswith sensor and communicationtechnologies for spacecraftthat can generateresponsesthroughdetection,diagnosis,reasoning, and adapttosystemfaultsinsupportofIntegratedIntelligentVehicle Management(IIVM)[19].Likewise,thisframeworkincorporates technologies which can deliver a continuous, intelligent, and adaptive health stateof anembedded systemand managethis informationtodevelopsafetyandreducecostsofoperations.So theIVHMcharacterizesalogisticsupportforthetransportprocess andinfluencesitslevelofreliability[20].However,therearesome technical challenges (tool,platform and integrationissues) and organizational challenges (structural and cultural problems) of implementing IVHM onlegacy platforms.Thecategorization of commonest typesoforganizational challengesfacewhen retro-fittingIVHMhasbeendescribedbyarecentwork[21].

2.2. Thetypesofcollaborationandtheirconstraints

Thesehumanandautomatedactorsmaycollaborateinvirtual communitiestoaddressspecificproblems,suchascollaborative troubleshootingordeploymentoftheremoteassistanceexpertise oftheorganizations.Thiscollaborationallowspotential improve-ments in maintenance on various activities (failure analysis, documentation of maintenance, fault diagnosis/location and repair/reconstruction)thatcanbebrieflydescribedasfollows[22]: Failure analysis: developments in sensor technology, signal processing,ICT(InformationandCommunicationsTechnology) and othertechnologies related tomonitoring and diagnostics

enablestakeholdersmaintenancetoimproveunderstandingof the causes of the failures, faults of the system and better monitoringandsignalanalysismethods,materials,techniques, design and production of better quality: tomove from fault detectiontomonitorthedegradation;

Documentation of maintenance: information such as the completionofthetaskcanbecompletedoncebytheuserand senttomultiplelisteners(softwareoractors)thatrecordedfor suchanevent. Anexampleis thebottleneck ofmassive data betweenproductionsystemsandbusinessmanagement,which can now be eliminated by converting raw data of the state machine,dataonthequalityofproductsanddataontheprocess capability in the information and knowledge for dynamic decisionmaking;

Faultdiagnosis/location:thefullimplementationofweb-based mechanismsprovidesexpertstodiagnosefaultonlinewiththeir

mostsignificantexperiences,andproposessolutionstooperators ifan abnormalconditionoccursonthe equipmenttoinspect [23]. Feasible solution of the expert can be contextualized; quality of the shared information can be improved and, consequently,reducesthetimetoresolution.Allthesefactors contributetoincreasingtheavailabilityofequipmentandmeans ofproduction,toreducemeantimetorepair(MTTR);

Repair/reconstruction: costly systemdowntime and mainte-nanceexpensecouldpotentiallybereducedbydirectcontactand interaction(troubleshooting)withdesignersand specialistsof theoriginalsystem.Forexample,diagnosis,maintenancework carriedoutandpartsreplacedaredocumentedonlinethrough structuredresponsesinthejobstepsthatareexplicitlydisplayed insystemssupport.

Maintenance services of the industrial organizations are structured as a network of resources and competencies which aredistributedamongtheunitsanddomainsoftheengineering. Collaborative Networked Organizations (CNO) are driven or constrainedbysomefactorssuchastheinteroperabilityprinciples, internal association mechanisms, dynamic inter-organizational rulesandgoal-drivenpolicies.ThemacroscopicbehaviorofCNO canonlybefullyunderstoodbythedescriptionofitslifecyclewith the following main stages [3]: (i) creation (initiation and foundation), (ii) operation (stable functioning), (iii) evolution (minor changes) and (iv) dissolution (termination of temporal existence)ormetamorphosis(majorchanges).

Besides, one major reason for the slack advancement in applying maintenance technologies to complex systems is the presenceofuncertaintyineverystepofthereliabilityassessment processand systemsafety. Inapplications ofoperative mainte-nancesystems,thesourcesofuncertaintymaybeclassifiedinto environmentalandoperationaluncertainties(e.g.weather,loading conditions);scenarioabstractions(e.g.subjectivedecisions,lackof knowledge); system uncertainties (e.g. non-linearity, boundary conditions, complexity); signal processing uncertainties (e.g. sensors,datafusion,decision making);andmodel uncertainties (e.g.form,parameters)[24].

Moreover,becauseofthefinancialconsiderations,ithasnow becomethepracticeforapolicyoftaskshiftingthataimstoentrust aless-qualifiedgroupofprofessionalswiththetasksofahigher level group and to build their capacities through training and supervision.Solessqualified workerstakeactionatthelevelof routinetasksandtheexpertopinionsandinterventionsofwhich will assistthemin complex important duties.Providing expert consultationsforbothoperatorsandmaintainerononesiteisstill an interesting approach, but it is expensive and experienced knowledgeisinmostcasesunavailable,incompleteordistributed. Weareinterestedinsolvingcomplexproblemsinmaintenance and we will discuss how can be solicited and managed the

knowledgeofstakeholders(operators,maintainersandexperts)to contribute to the improvement of the collaborative decision-making.Primaryresources (operatorsand maintainers) provide thestructuralflexibilityfortherapidandtemporarydeploymentof maintenanceactions toundertakeassessment missions whena problemisdetectedortomonitorandadviseonthe implementa-tion. Many local primary resources do not cover the complex situationsthatrequirededicatedresourcesandexperiencedactors. Industrial organizations should reinforce their capacities in advanced activities, with a view to advanced studies, in close interactionwithexperts in thefield ofmaintenance, and share theirbestpracticestogether.Theexpertresourceswithsustainable methodologiescanhelporganizationsachievemeasurableresults andfasttimetovalue,whileaddressingthemiddleorlongterm needsofcomplexsituations.Theexpertisetendstobeexpensive andnotwidelyavailableinmanyindustrialsituations.However, the longera decision takes, the more critical it becomes. This decision-making may involve pooling of specialized resources, working collaboratively toward a similar goal, or sharing of information. It can be considered that in typical situations of maintenance activitiesand complexproblem solvingstrategies, thecollaborationiscarriedoutasfollows(Fig.1):

anactorAitriestosolvetheprobleminitsowncasebase.Ifthe problem solving action is unsuccessful, he broadcasts the problemtootherexpertswhoreturntheirsolutiontoAi.

theactorAiselectsthemostsuitableactorAjfortheresolution, accordingtoanassessment calculatedfromtheexpertiseand skillsofdifferentactors.Thus,Aiobtainsthesolutionproposed

forAjandactslocallytosolvethecases.

Patterns and processes of maintenance deployment and managementwillgiveprioritytotheuseofalternative communi-cationsystemsandaccesstorelevantknowledgeinthecontextof crisismanagement.Theexpertscanofferorganizationalbackupfor complex maintenance and valuable local assistance in crisis situations.Theintroductionofcommonknowledgerepresentation formalismwillbeveryinterestingtoimproveexchangesbetween distributed actors and enhance workflows understanding. The reasoning mechanisms of this formalism will be useful for mismatchingdetectioncalculationsaswellasforbest collabora-tivereasoningidentifications.

3. Cognitiveexperiencefeedbackforindustrialmaintenance management

In general, diverse studies have concentrated on distinct features of expertise effects on cognition, includinghypothesis generationandevaluation,knowledgerepresentationsunderlying performance, diagnostic reasoning and the organization of decision knowledge. The development of expertise involves experience-based learning (i.e. through exposure to real situa-tions)andeachprogressingphaseischaracterizedbyfunctionally differentknowledgestructuresunderlyingperformance. Addition-ally,toacertaindegree,expertreasoningisbasedonthesimilarity between thepresenting situationand someprevious situations availablefrommemory.

Often, information and maintenance data (system’s states, procedures,protocols, etc.) arevariously formalizedand stake-holders’ knowledge (business rules, normative requirements, standards,etc.)israrelyexplicit,which makesthemdifficultto usewithintheirimmediateframeworkand,afortiori,inshifted contexts.Thiscanbecriticalifitrelatestodelocalizedunits,with limitedmeansandwhoseexperiences(pasteventsprocessing)are few (resulting,for example,from distributed architectures and infrastructures).

3.1. Proposedmethodologicalframeworkformaintenance management

A collaborative approach for maintenance management will allowstakeholderstoworktogetherandleadtobetter decision-making processes in integrating knowledge of each business domain.

Theapproachdevelopedbelowistoformalizethe implemen-tation of knowledge based industrial maintenance, using CG’s mechanisms that appear suited to handle these collaborative situations.Theapproachinvolvesthreesteps(Fig.2):

Cognitivemodelingofexperiencefeedback:therearedifferent typesofEF,fromtheuseofelementarystatistics(MTBF,MTTR,

Fig.2.Formalapproachofexperiencefeedbackformaintenancemanagement. Fig.1.Collaborativeframeworkformulti-expertmaintenance.

etc.), tothelatestmethods ofcognitivescience basedonthe capitalization of experiences and knowledge [25]. In this cognitivescienceapproach,weproposethemodelingofexpert knowledge of actors in order to assist the problem solving processesinacollaborativeworkingenvironment.Thisapproach isbottom-up:knowledgeisgraduallydrawnfromrelevantcases initiatedbytriggeringevents(hazards,disruption,calendardata, etc.)[26].Thecapitalizationofcasesallowsforthepoolingofthe distributedexpertise;

Knowledgeformalization:theinformationandcommunication (relatedtheorems, indicatorsorassessment criteria, etc.)that ensurefromthe collaborating actorsin the courseof solving decisionproblemconstituteknowledge.Theexperts’knowledge generated in step 1 is translated into a formal specification expressedintheCGformalism.Thisformalismprovidesavariety ofreasoningmechanisms[27],whoseuseisdetailedinthenext section,whichwillallowto[28]:(i)verifythecorrectexpression of knowledge, (ii) ensure the overall consistency of expert knowledge, (iii) detect ‘‘shortcomings’’ requiring additional information, (iv) assist in communication and information exchangeenablingactorstoworkinadistributedenvironment. Exploitationinmaintenance:theproblemsolvingmethodofa groupofactorsandtheresultinganalysiswithsolutiontotheir problemareformsofknowledgethatcanbecapitalized[15].The formalizedknowledgeismadeavailabletothemaintainerinthe formoftechnicalsheetscontainingstructuredrules,constraints and good practice guides. Their exploitation of maintenance operationswillinterveneinsupportofthemaintainerinorderto ensurethat theequipmentunderhis responsibilitywould be abletoprovidetherequiredservice.Forinstance,itwillprovide guidanceonspecific aspectsof maintenance in order tohelp manageriskeffectively.

Our work fits into the scheme of the experience feedback framework detailed in [29]. In this framework, a structured descriptionofgradualtransformation,byactors,ofaneventinto knowledge is suggested. Using a collaborative approach, the proposed methodology seeks to promote the involvement of semanticmodelinginidentifyingnotonlythedefectiveequipment butalsoindeterminingsimultaneouslythesignificantfactorsthat influence the success or failure of the industrial maintenance management.Thecase descriptionhasthreecomponents (‘‘con-textualcase –experience–knowledge/lessonslearned’’)froma cognitive experience feedback process. These components are describedasfollows:

-thecontextualcaseprovidesageneralpictureoftheproblemto solve for enabling context reasoning and prior to in-depth analysis[30].Itcontains forinstancethedescriptionoffaulty equipmentanditsuseconditionswhentheproblemoccurred; -theexperiencelevelleadstotheanalysisandimplementationof

solutions for thecontextual events:search of thecauses and evaluation ofthe effects onthe systemtopropose corrective actions;

-the‘‘knowledge’’levelsummarizestheinvolvedanalysisthrough knowledgebroughtbythedomainexpertsandgeneralizedrules fromthissetofexperiences(e.g.rulesfromaccident investiga-tionsforsustainablesafetyimprovements)[31].

Complexity considerations of today’s intelligent machinery maintenance systems might lead to fault detection involving severaltechnologiesandusingdifferentdiagnosticmethods.These methodscanbedesignatedasfollows[32]:

-electromagneticfieldmonitoring,searchcoils,coils; -woundaroundmotorshafts(axialflux-relateddetection);

-temperaturemeasurements; -infraredrecognition;

-radio-frequency(RF)emissionsmonitoring; -noiseandvibrationmonitoring;

-chemicalanalysis;

-acousticnoisemeasurements;

-motor-currentsignatureanalysis(MCSA);

-model,artificialintelligence,andneural-network-based techni-ques.

During maintenance operation (e.g. diagnostic stage), it is common that the involved maintainer seeksthe support of a delocalizedexpert.Forsystemswhosefunctionalrequirements include several technologies (e.g. a rotary machinery system), useful knowledge is often distributed among several experts fromdifferentfields.Theacquisitionofknowledgefrommultiple expertscanbeastimulatingsituation;thenagainmanybenefits canalsobeobtained(e.g.enhancedunderstandingofknowledge domain, improved knowledge base). Sometechniques (Delphi method,nominalgrouptechnique,analyticalapproach,etc.)for facilitating knowledge acquisition from multiple experts are commentedin[33].WearguedthattheDelphi-basedknowledge acquisitionprocess[34]isareasonableapproachforconducting knowledge acquisitionin collaborative maintenance problems forthetworeasons:(i)itprovidesamechanismfor reconcilia-tion of asynchronous conflicts between multiple experts asynchronous,and(ii)itfacilitatesinteractionamong geograph-ically dispersed individuals in collaborative organizational systems.

We can place our approach in maintenance context with regards tofleet considerations[20]. This option can be imple-mentedonagradualbasissincethefleetmodelissuitablefora distributed system with each component serving collaborative actionsinthesettingofindustrialmaintenance.Fleetcomposedof similarorheterogeneouscomponentscanbeexploitedtoacquire knowledgeortofindrelevantinformationtobereused.Experts basicallydeterminewhetherasituationisreusablebythegeneral approachofanalogicalreasoningwhichprovidesinformationon some similar characteristics of the component analyzed. This improvesourinsightintocomponenthealthand statusthrough theanalysisofeventssuchassolicitationresponsesorprogram participation.Accordingly,thecomponentobtainsinformationon thehealthstatusoftheothercomponentsthatcanhelpthefleet management toupdateonthestatus of real-timemaintenance serviceand current monitoringprocess.So it is possiblethat a componenthighlightedthedifficultiesexperiencedin collaborat-ingwithitsneighborsandwecanmanagetolimittheseverityand extentoforganization disturbancesand impairmentto mainte-nanceschemefunctioning.Fleethasshownthatoneofthemost reliableindicatorsofacomponent’shealthstatusis,quitesimply, thecollaborative assessment of their health by itsneighboring components.

Likewise fleet modeling is important for the enrichment of knowledgeandtheadvancementofbettermanagementataglobal level. Thisfleet consideration presentsa broaderinterpretation involvingvariouscollectedcasesthatwillcontributetoexpandthe body of knowledge on maintenance and to further enhance adjusting monitoring of diagnosis-related activities in mainte-nance.Therefore,variousformsoflessonslearnedareextensively organizedtoenrichthepracticalknowledgeofmaintenance,since theanalysisofcasesobtainedseekstodevelopknowledgethat informspeople beyondthespecificsituationinwhich thework was conducted. The fleet dimension in industrial maintenance involvesgeneratingknowledgeandunderstandingamong collab-orative actors about the target system, its failure modes with preventionandmanagementpractices.

Thismeansthatatmanyaspectsofreliability,such consider-ationsmustbeprovidedwiththewaystocheckthataconsidered componentspopulationisingoodworkingorder;thecomponents failures with associated root causes analysis must be made apparenttothedomainactors.

Inourproposition,experienceddevelopmentisunderstoodas theprocessoftranslatingknowledgegainedthroughexperience feedbackintonewscontextualsituations.Thespecificfeatureof thisapproachresidesintheengagementof graphoperationsto describevisualreasoningwithanunderpinninglogicalsemantic model(domain ontology).Inthisconnection,a morefleet-wide ontology approach can be used to formalize knowledge by describing the technical characteristics of the system/sub-sys-tem/equipment, thedegradation modes,degradationindicators, themissionandtheenvironments[35].

Asa matteroffact,throughinformation fromaCNO, health indicators can deepen the understanding of health monitoring issuesinacomponentspopulation,orcurrentindividuals’health statusinabroadermaintenancecontext.Theexistenceofsucha link of information and assessment is a way to reflect the performanceofcollaborativecomponentsandensureacceptable performancelevelsintheoperationalprocesses.Insum,theCNO intends toserve as a channel of information for thebenefit of maintenancethatusetheirknowledgeofcollaborativeissues to assessthestatusofactorsinvolved,andtoconverttheinformation gatheredintousefulmaintenanceintelligence.

3.2. Reasoningspecificationincollaborativemaintenance

Ifoneconsidersthattheoperatingstateequipmentistranslated asavector‘‘signature’’thenitsinterpretationprovidesinformation onthisstate (orthefailure factors,potentialfor damage, etc.). Some researchdescribes theapplicationof operationaltoolsfor searchingandanalyzingahigh-volumedatastreaminthefieldof maintenance (e.g.machinemonitoring, reliabilityanalysis,fault detection and tool condition monitoring) [36]. Meanwhile, a collaborative approachincomponentsanalysisofthissignature will often allow a more efficient diagnosis (speed, accuracy, precision,etc.).Indeed,throughtheirexperiences,actorsinvolved will perceive the considered cases with alternative views and constructdifferentknowledgefromtheirownrulesofexpertise, enriching theoverall diagnosis.Thus, the actors collaborate to analyze the relevant information and to identify pieces of knowledgethatcanbeusedtoguidetherequireddecision.The essential problem in the collaborative approach is to avoid contradictionandinconsistencyamongdifferentdomain knowl-edge.However,twomainconfigurationsmayariseincollaborative situations: (i) compatibility of reasoning (i.e. the experts use differentproblemsolvingmethodsbutobtainnon-contradictory results) and (ii) incompatibility of reasoning (i.e. the different problemsolvingmethodsusedbytheexpertsleadtocontradictory results). The formation of a single knowledge repository can integrateallexpertviewpointsrequiredformaintenance,butitis essentialtostudypossiblelinksbetweentheseexperts’reasoning todetectconflictsorcomplementaritiesbeforeintegratingitintoa collaborative decision making. In this purpose, the knowledge formalizationofmultipleexpertsinmaintenancewiththeCGwill betwofold:

toclearlyspecifytheexpertknowledge(repairing,adjustment, servicing, monitoring and verification of equipment) that supportsthepositiveeffectofimprovingmaintenance manage-mentinacollaborativemulti-expertenvironment;

to promote knowledge sharing, comparison, or even the generationofnewknowledgewithintheknowledgerepository shared by different actors involved in collaborative decision

makingprocessesassociatedwiththemaintenanceof consid-eredsystems.Theglobalaimistoachieveabetterfoundationfor makingtherightdecisions,andtherebyreducingtheamountof unplanned maintenance tasks and subsequently unplanned shutdowns.

TheMulti-Agent-Systems(MAS)canofferatechnicalsupport for negotiation based on the sharing of various knowledge in remotemaintenancedecision-makings.Forexample,a Problem-Oriented Multi-Agent-Based E-Service System (POMAESS) has beenproposedtofacilitatecollaborationofmaintenanceprocesses and experts in remote service maintenance [37]. POMAESS integratesCBR-baseddecisionsupportfunctionandalsousesthis componenttoprocessandmanageinformationofcompetingor complimentaryexplanationsintheservicemaintenanceproblem solving. The dynamic simulation offered by the MAS for collaboration between the stakeholders makes it a valuable reference tool. However, some parameterization techniques should be extended to include the qualitative characterization of knowledge structures for the management of intangible resources or with regard to policies dealing with experience feedbackprocesses.

Case-Based-Reasoning(CBR)isaprogressivelearning method-ology which incorporates artificial intelligence (AI) techniques wheretheend-userisengagedtodealwithanumberof work-related cases in order to learn how to react well to various challengingsituations.UsingCBRitispossibletotransformthe description of the maintenance process into a problem-solving model. Particularly, in thecollaborative process, CBR makes it possible to take to the experienced knowledge and acts as a supportsystemfor workingin concreteterms,in botha multi-lateralandmulti-actorway.

Anessentialpointinsolvingcomplexproblemsrequiringthe collaborationofexpertsisthewayinwhichcasesareorganizedin the case base. The distributed Case-Based Reasoning (CBR) is presentedasaperformanceimprovementsolutionofclassicalCBR systems[38].Weplaceourselvesinthecontextofa distributed CBRarchitecture.AdistributedCBRcanbeusedintwotypesof situations:

-asingleagentwithaccesstodifferentdatabases inwhichthe learnedcasesaredistributed;

-severalagents,eachwithitsownbase,collaboratingtosolvethe problem,eachagentconductsthereasoningatthelocallevel.

Ourproposal is based onthe principleof ‘‘distributed’’ CBR architecture(Fig.3).Thegoalis toreuseand combinedifferent casesassociatedwithreasoningfromlocalcasestogeneratenew solutions to complex problems addressed by a collaborative decision-makingprocess.Itwouldalsopermitalargenumberof actorstoprofitfromtheexperiencefeedbackandthebuildingupof knowledge within a wide network. Several advantages arise: improvementoftheknowledgeandskills,greaterefficiencyofthe reasoning process (and extension of the action spectrum) and greatereffectivenessinaiddelivery andimprovedcollaboration environment, which will ultimately result in a better use of appropriatecasemanagementmethods.

CollaborativeCBRplatforminducedwillhavetheparticularity to combine different case bases CBi (i=1,..., n, n: number of experts) intoa commondatabase<CB>illustrating a series of experts,characterizedbytheirareaofcompetenciesandcombined tosolveaproblem(bynegotiation,cooperationorcollaboration) and establish a solution to this problem. Such improved CBR platformshould,amongotheradvantages,leadtothe harmoniza-tionandstreamliningoftherespectiveworkingscenarioswithin themaintenance,andshouldenabletheactorsofthecollaborative

organization to contribute substantive inputs to the problem solvingprocesses.

Theinvolvement of experts canbe consideredbetween two extremescenarios:

-partition of theproblem intodisjoint subsets;each expert is responsible for a part of the resolution of the problem, the solutionintegratespartialresponse;

-submissionofthecompleteproblemtoeachexpert;thesolution aggregatescompetingresponsesoristheresultofaselectionof thesesolutions.

In terms of architecture described in Fig. 3, a possible orientationcombinesdifferentCBiin a classicalorganization of

CBRfocusedonthecommon<CB>,introducingonlyafurtherstep intheprocessingcycle: aggregationofexpertadvices.Thisstep helpstodevelopageneralsolutionfromdifferentpointsofview providedbytheinvolvedexperts.

Thespecificityofconceptualgraphsrepresentationofhuman expertisewillbenefitfroma logical backgroundunderlyingthe semanticmodelingframeworktostimulateandsupport collabo-rative actions. Furthermore, the descriptions of the reasoning become reproducible in a graph-way for better interaction between collaborative actors. The logical background ensures suitable knowledge representation and formalization for a comprehensiveand coherentimplementationoflessons learned fromexperiencefeedback.

3.3. Knowledgerepresentationformalisms

Thereisadiversityofknowledgerepresentationlanguagesthat include mainly the graph-based approach [39](e.g. conceptual graphs and semantic networks) and the frame-based approach [40](e.g.FramesandDescriptionsLogics(DLs)):

the frame-based approach represents knowledge using an object-likestructure (e.g. individualelements and their orga-nizedclasses)withattachedproperties.Thesemanticsofframes are not entirely formalized, whereas the fully defined set-theoreticsemanticsofDLssupportspecializeddefineddeductive services(e.g.knowledgeconsistencyandinformationretrieval). the graph-based approach represents knowledge as labeled directgraphs,wherenodesdenoteconceptualentities(concepts orrelations)andarctherelationshipbetweenthem.Semantics networkssufferfromthelackofaclearsemantics,whereasthe underlyinglogical semanticsof conceptual graphsprovides a diagrammatic reasoning service allowing sentences that are equivalent to first order logic to be written in a visual or structuralform.

Thegraph-basedapproach(ofwhichconceptualgraphsarea keyrepresentative)hasadvantagesover frame-basedmodelsin expressingcertainformsofmodeling(e.g.mappingpropertiesinto nestedcontexts)andinprovidingavisualreasoningthatfacilitates anintuitiveunderstanding.Inaddition,conceptualgraphscanbe easilytranslatedintotheterminologyofsomeotherapproachesin knowledgeengineering,suchasResourceDescriptionFramework Schema(RDFS)[41]anditsevolution,theWebOntologyLanguage (OWL)[42]mainlyappliedinconnectionwiththeSemanticWeb framework[43].Asaresult,itgeneratesthepossibilitytointeract andexchangethemodeledknowledgewithinternalandexternal collaborators.

The various forms of reasoning used in maintenance often include expert rules that must be shared by actors in a collaborative decision-making environment. These expert rules reflectthe compliancewithvariousregulations and constraints (legalrequirements,organizationpolicies,orbusinesspractices) thatguidetheactionsofmaintenance(scheduledorunscheduled). Inthispaper,weutilizea visuallanguage,CGformalismforthe Fig.3.ArchitectureofCBRintegratinginvolvementofcollaborativeexperts.

specification ofexpertrules, which isstillgrounded on mathe-maticallogictoenableformalreasoning[16].TheMULTIKATtool [44]hasexperienceda methodthat allowsthecomparisonand integration of coherent conceptual graphs modeling different viewsofexpertise.Theintegrationisbasedonsomeelementary operations(specialization, generalizationand join)that support the transformation of graphs. The comparison is based onthe projectionoperationthatallowsthesearchofsemanticproximity betweengraphrules.Thesefeatureshelptomanageconflictsof rulesandconsequencesofchanges,suchasaddinganewexpert knowledge, in a knowledge base common to several experts. Ultimately, the decision maker can effectively exploit a set of knowledge rulesintegrating differentviewpoints related tothe analyzedproblemandmaintenanceobjectivesset.

4. Knowledgeformalizationwithconceptualgraphs

Theconceptualgraphformalismisconsideredasacompromise representation between a formal language and a graphical language, because it is visual and has a range of reasoning processes[27].Conceptualgraphscanbeusedinmanycomputer science areas, including text analysis, web semantics, and intelligentsystems[45].Furthermore,othernotationsfor describ-ing processesand events-flow-charts,state-transitiondiagrams, dataflowdiagrams,Petrinetsorstatechartscouldbetranslatedto nestedconceptualgraphs[46].Interestinglyenough,statecharts have beenexperimented asa mediationtool between multiple expertsandaknowledgeengineer,specificallywhenexpertiseis notparticularlywelldefined[47].

4.1. Formalismpresentation

Theconceptualgraphformalismisaknowledgerepresentation languagewhichhasawell-definedsyntaxandaformalsemantics thatallowsonetoreasonfromitsrepresentations.

Definition:A simpleconceptualgraphis afinite,connected, directed,bipartitegraphconsistingofconceptnodes(denotedas boxes),whichareconnectedtoconceptualrelationnodes(denoted ascircles). Inthealternative linearnotation, conceptnodesare written within []-brackets while conceptual relation nodes are denotedwithin()-brackets.Theconceptssetandtherelationsset aredisjoint.

A concept is composed of a type and a marker [<type>: <marker>], forexample [Failure:short-circuit_09].The type of concept represents the occurrence of object class. They are grouped in a hierarchical structure called a concepts lattice showing their inherit relationships. The marker specifies the meaningofaconceptbyspecifyinganoccurrenceofthetypeof concept.

Aconceptualrelationbindstwoormoreconceptsaccordingto thefollowingdiagram:

[C1] (relation’sname) [C2](meaningthat‘‘C1isrelatedtoC2

bythisspecificrelation’’).

Intheanalysisofmaintenancemanagement,themostcommon relationsaredependencyrelations,specifically,causal, condition-al,temporal,andBooleanconnectives,suchasAND,alternating-OR and exclusive-OR relations. Anexample of conceptual graph is showninFig.4:aserviceoutageiscausedbyaservicefailure;an instanceofcriteriafordeterminingtheclassesoffailureseverities isforavailability,theoutageduration.EachCGcanbetranslatedto logicalformulas.ThelogicalinterpretationofagraphGinFig.4is definedasfollows:

F

(G):

9

x,y,z(Failure(short-circuit_09)^Serviceoutage(x)^ Duration(y)^ Availability(z) ^Agent(x, short-circuit_09)^ characterization(x,y)^Influence(z,y))

Aderivationisafinitesequenceofelementaryoperationson oneorseveralnodes(conceptsorrelationnodes)ofaCG.Examples ofsixelementaryoperationsare:

Binary

-

Relation

Before During Temporal-relation Usual-relation Spatial-relation Logical-relation Agent Characterization Object Implication Influence

Concept

Repairs Modifications Maintenance Availability Maintainability Attributes Threats Faults Errors Failures States Service delivery Service outage Service shutdown Time points Time intervals Duration States

Threats agent Usual-relation Duration

Attributes influence Outside

Inside

Service outage

Failure: short-circuit_09 agent characterization Duration

Availability influence

Specialization Generalization

Simplify:thisoperationdeletesaduplicatenodeanditsinverse isthecopyoperation.

Restrict:this operation decreases a node label(node type is replacedbyoneofitssubtypesorthegenericmarkerisreplaced by an individual marker) and its inverse is the unrestrict

operation.

Join: thisoperation mergestwo nodesinto a singlesuper or equalnodelabelanditsinverseisthedetachoperation.

Aderivationisafinitesequenceoftheseelementaryoperations thathaveaformalsemanticsbasedonalogicalinterpretation.Asa result,themeaningofgraphoperationsisdeterminedinlightof the derivation to be applied, based on a logical interpretation whichgivesfulleffecttothevisualreasoning.Thederivationhas one of three conceivable properties on thelogical relationship betweenastartinggraphuandtheresultinggraph

v

[48]:

Equivalence: copy and simplify are equivalence rules, which generateagraph

v

thatislogicallyequivalenttotheoriginal:the

knowledgeofuisincludedin

v

andtheknowledgeof

v

isincluded

inu(logicalmeaningu

v

and

v

u).

Specialization:joinand restrictarespecializationrules, which generateagraph

v

that impliestheoriginal: ucontains more

specificknowledgethan

v

(logicalmeaning_u

v

).

Generalization:detach and unrestrictare generalizationrules, which generate a graph v that is implied by the original: u

containslesspreciseknowledgethan

v

(logicalmeaning

v

u).

Ontologicalknowledgeprovides a formaldescriptionof the maintained system (experiences and lessons learned) [49]. Expansionpossibilitiesarepreservedbythemodular organiza-tion of considered concepts; we can find, for example, a maintenance division into three levels: general principles of maintenance(resource, event, etc.), in any particular domain-work, (petrochemicals, energy, transport, etc.), for a specific service.Severalontologicalmodelsareavailableformaintenance management such as those proposed by the IEC/ISO62264, MIMOSA/CRISandfederatedin theOpenO&M(Operations& Maintenance).Byprovidingastructuredandcontrolled vocabu-lary,theyassureaconceptualizationrelevanttothe understand-ing and processing of maintenance problems. The ontology modelingisaniterativeprocesswithrespectinaccordancewith fourontologydesignprinciples[50]:domainclarity,application of the identity criterion, identification of a basic taxonomic structure and explicit identification for roles. Folksonomies

(collaborativetagging)suggesta collaborativeinformalway of onlineinformationcategorisation,searchandsharing[51].They are a faceted classification scheme and provide a modeling informationbottom-upthatenablestheemergenceofsemantics fromalabelingoflotsofthingsbypeoplethroughshared sub-communitiesofinterest[52].

Inthesecommunitiestheusershavesimilarinterestsand/or domain expertise, with explicit links (through social networks theirusersaremembersof)orimplicitlinks(throughthesharing ofthesametaggingschemes,tagsand/orobjects).Therearesome attempts toreconcilethestandardization,automatedvalidation andinteroperabilityofontologieswiththeflexibility,collaboration andinformationaggregationoffolksonomies[53].

4.2. Compilationofconceptualgraphsrules

Letusconsiderknowledgebases(KB)composedofasetoffacts (existentiallyclosedconjunctionsofatoms)andasetofrules.Let

Rs be a set of rules in the form H!C, where H and C are conjunctions of atoms, respectively called the hypothesis and conclusionoftherule.Theconceptualgraphoperationsallowthe

representationofderivationrules,andtheeffectiveapplicationof theserules,accesstoasetoffactswithconstraints[54].Thegraph rule is used in theclassical way; given a simple graph, if the hypothesisoftheruleprojectstothegraph,thentheinformation containedintheconclusionisaddedtothegraph.

4.2.1. Logicalsemantics

Ithasbeenshownpreviouslythatconceptualgraphrulescanbe described by means of first-order logic augmented with the temporaloperators[27].AconceptualruleR(G1)G2)isapairof

l

-abstractions(

l

_x1,...,xnG1,kx1,...,xnG2),wherex1,...,xn,called connectionpoints,enableonetolinkconceptverticesofsamelabel of G1 and G2. The logical interpretation of a conceptual ruleR

(G1)G2) is defined as follows:

F

(R)=

8

x1,...,

8

xn

F

(

l

x1,...,

xnG1))

F

(

l

x1,...,xnG2).Thesemantics

F

(providedin[39])maps eachSimpleConceptualGraphGintoafirstorderlogicformula

F

(G).Whena ruleis appliedinforwardchainingtoaconceptual graph,theinformationoftheruleisaddedtotheconceptualgraph.

4.2.2. Graphofruledependencies

AruleR0₂

RsissaidtodependonaruleR2Rsiftheapplication ofRona factmaytrigger anewapplicationof R0_.Buildingthe

optimalgraphofruledependencies(notationGRD(Rs))allowsone toimprovetheefficiencyofthecompilationofarulebase[55]. Concretely, in classes of rules for which forward or backward chaining mechanisms are finite, the structure of the GRD(Rs)

providesaneffectivemethodfordeterminingtheexistenceofa rulededuciblefromtheKB.Inpracticalterms,ifRsadmitsafinitely combinedpartition,thenitissufficienttoyieldthedecidabilityof thedeductionproblem[56].

LetRsbeasetofrulesprovidebydomainexperts,firstlywe makeapartition(Rs1,...,Rsn)betweenrulesaccordingtoexpert groupsof compatibleviewpoints. Secondly, withineachRsi, we make a finitely combined partition based on the notion of dependenciesbetweenrulesthatobeysomeconstraintpreserving decidability. Such partitions are interesting as they permit to reasonsequentiallyandindependentlywiththetwolevelsofsets ofrules.

4.2.3. Exploitationofconceptualgraphsformulti-expertisein maintenancemanagement

AformalknowledgemodeledbyCGsinexperience feedback processes can be a very useful tool for conveying an accurate meaningtoa collaborative workenvironmentbetween domain experts [57]. For a given application, several viewpoints of expertise may be engaged in combination. For example, some investigationstoimprovetheavailabilityof arotary machinery systemcan involveexpert knowledgein mechanics,electricity, cyberneticsandremoteaccesscomputing.Itwillbeconstructed foreachknowledgeexpertruleassociatedtoaspecificdomain,a conceptualgraphrule.Inthemaintenancemanagementcontext, eachrulehasasymbolicformH!C,whereHisaCG context’s hypothesispartandCisapartCGanalysis’conclusion.Forbinary relations,thebasicrelationshipisasfollows:Cin!(rel)!Coutalso

markedbyCin1!(rel)2!Cout.

During the knowledge modeling phase of the maintenance rules,theuseofCGpropertieswillhelptoenrichthemaintenance knowledgebaseinordertoeasetheiraccess,sharingandreuseby themembersoftheindustrialmaintenancemanagementintheir individualandcollectivetasks[58].ThebaseofcanonicalCGstoa maintenanceproblemcanbepartitionedaccordingtoviewpoints correspondingtoschoolsofthought.Forthefusionofexpertrules, thegeneralideaistoapplyaconjunctiveprocedurewithingroups of compatible viewpoints, and a disjunctive procedure across groupsofcomplementaryviewpoints[59].Theseproceduresfor comparingtwographrulesarebasedoncriteriaofgeneralization

versusspecialization,andconceptualizationversusinstantiation. In thefollowing,we willexplaintheconstruction ofintegrated graphsrulesaccordingtothechosenprocedure:

Conjunctive procedure: the preconditions are that experts shareanexplanationofthemaintenanceproblemssincetheyare all reliable but not independent information sources. The conjunctiveprocedurereliesontheintersectingknowledgeof differentexperts. Thespecializationoperationis usedwhenan expertismorespecializedthantheothersonagivenaspect,and uses more precise expressions. The conceptualization is used when an expert focuses on particular cases and on specific examples,whiletheotherexpertexpressesgeneralknowledgeat a betterlevel ofabstraction.Thisconjunctiveproceduredeals withcontradictionasfarasthedegreeofconflictremainslow between experts withingroups having compatible reasoning. Theconjunction procedureproduces atrivial resultwhenthe informationsourcesconflictcompletely.Inthiscase,thefusion fallsintopurecontradictionwithincompatiblereasoning. Disjunctiveprocedure:thepreconditionsarethatatleastone

expert’s theory, but not all theories, is a reliable information source.Theexperts’rulesdescribeindependentand complemen-taryviewpoints.Insuchcase,itisimpossibletomakeaselection betweenbothexpertrules.Thedisjunctiveprocedurereliesonthe connectionofthegraphrulesbytheircommonconceptsinthe maintainedequipmentwithmaximaljointoperation.

Thishighlightstheinterestofstudyinginteractions between rules inordertobettercharacterizetheirdependenciesandthe analysis of combined information. We therefore have the opportunity to apply graphs operations not only for curative purposesinthemaintenance,butalsoforpreventivegoalsinorder toavoidsimilardifficultsituations(failureordamage)infuture. Experiencegainedandlessonslearnedfrominitialproblemsolving developments will be applied as soon as possible in similar situationsinothercollaborativemaintenances.

5. Applicationexample:arotarymachinerysystem

WeillustratetheproposedapproachusingCGswiththecase studyofarotarymachinerysystem(Fig.5).Onedifficultyinsucha systemistheheterogeneityofthefailurelawsofmechanicaland electrical components which are, furthermore, integrated into modules equipped with sensors and actuators. Each system’s componentissubjecttoanindividualpatternofmalfunctionand replacement,andallpartstogethermakeupthefailurepatternof theequipmentasawhole.

Therotarymachinerysystemthatwe considerhereistaken from[60]inwhichtheauthorswereinterestedinthereliabilityin ordertodetectearlieranyimminentfailuresandreducerisks.The mainstream of the involved maintenance applications focuses moreoncommonrotarymachinerycomponents,suchasbearings, gears and motors.Table 1 illustrates possible failure modes of

thesecriticalcomponents,withtheircharacteristicsandcommon detectiontechniques.Themaingroupsofdetectiontechniquesare mechanical, like vibrations and acoustic, and electrical, like currentsandvoltages.Othersdifferenttechniques,suchasacoustic measurementsorchemicalanalysisarealsoengagedtoinvestigate the natureand the degreeof the fault. Itis also important to identifythemostfrequentfailureinitiators,failurecontributorsand

underlyingcauses[61].

5.1. SystemmodelingwithCGs

Themaintenancetaskscanbeinitiallyevaluatedbydifferent specialists of the involved fields. As the lessons are learned, a knowledge-base can be generated and used to guide possible maintenanceactionsandoperatingscenarios.WepresentinFig.6 asampleoftheexpertrulesrelativetothefailureprocessesofa rotarymachinerysystem.Specialistsaregroupedaccordingtothe theoretical or experimental connections of their scientific dis-ciplines:forinstanceweputtogethermechanicaland thermody-namicsspecialists in onegroup,while materialsand chemicals specialists in another group. In association with electrical specialists,thefirstgroupconcentratesonthefailureinitiators, whereas the second group pays more attention on the failure contributors. Comprehensive underlying causes are identified collaborativelybyallthespecialistsoftheinvolvedfieldsofscience andtechnology.Expertrulesarelinkedwiththemaintenanceof commoncriticalcomponents.Here,afinitelycombinedpartition oftheusedsetofrulesis{{R1,R2,R3},{R4,R5},{R6,R7,R8}}.Theeight

expertrulesareexpressedseparatelyindistinctconceptualgraphs. A failure initiatoris theevent ormechanism that initiatesa process which couldpotentially lead tofailure or damage ina studiedsystem.Withouteitherthelatentconditionforfailureor thefailureinitiator,thesystemwillcontinuetorunreliably.Many mechanismsmighttriggertheonsetorexacerbationoferrororthe

Fig.5.Therotarymachinerysystem.

Table1

Characterizationoffailuremodesofcommonrotarymachinerycomponents.

Component Failure Characteristic Commonmeasures

Bearing Rolling-elementsandcagefailures,abrasion,fatigueand pressure-inducedwelding

Rawdatadoesnotcontaininsightfulinformation,low amplitude,highnoise

Vibration,oildebris, acousticemission Gear Manufacturingerror,toothmissing,toothpitting/spall,

gearcrack,gearfatigue/wear

Highnoise,highdynamic,signalmodulatedwithother factors(bearing,shaft,transmissionpatheffect),gear specsneedtobeknown

Vibration,oildebris, acousticemission Motor Statorfaults,rotorelectricalfaults,rotormechanicalfaults Currentsandvoltagesarepreferredfornon-invasive

andeconomicaltesting

Statorcurrentsand voltages,magnetic fieldsandframe vibrations

Context: rotary

machinery

failure

description

Bearing

Faults 1 object 2 implication

1 2

Analysis: failure initiator

description

Mechanical breakage Detection 1 attribute 2

Rule

3

- Thermodynamics

specialist

Rule

1

- Mechanical

specialist

Rule

2

- Mechanical

specialist

Context: rotary

machinery

failure

description

Bearing

Faults 1 object 2 implication

1 2

Analysis: failure initiator

description

Overheating Detection 1 attribute 2

Context: rotary

machinery

failure

description

Gearbox

Faults 1 object 2 implication

1 2

Analysis: failure contributor

description

High Vibration Detection 1 attribute 2

Rule

4

- Electrical specialist

Context: rotary

machinery

failure

description

Motor

Faults 1 object 2 implication

1 2

Analysis: failure initiator

description

Electrical Malfunction Detection 1 attribute 2

Rule

5

- Electrical specialist

Context: rotary machinery

failure

description

Motor

Faults 1 object 2 implication 1 2

Analysis: failure initiator

description

Insulation Breakdown Detection 1 attribute 2

Context: rotary machinery

failure

description

Motor

Faults 1 object 2 implication 1 2

Analysis: failure contributor

description

Persistent Overloading Detection 1 attribute 2

Rule

6

- Materials specialist

Context: rotary machinery

failure

description

Gearbox

Faults 1 object 2 implication 1 2

Analysis: failure contributor

description

Poor Lubrication Detection 1 attribute 2

Rule

7

- Chemical specialist

Context: rotary machinery

failure

description

Bearing

Faults 1 object 2 implication 1 2

Analysis: failure contributor

description

Aggressive Chemicals Detection 1 attribute 2

Rule

8

- Chemical specialist

developmentoffailure.Theproblemsolvinginmaintenanceisa decisiveissue,requiringbothadeepexaminationoftheunderlying causesandcarefulconsiderationofthemosteffectivemethodsfor achievingasignificantandsustainedreductioninfailureinitiators

andcontributors.Itisknownthatamaintenanceproblemisalways activated by some failure initiators and can be exacerbated by potentialfailurecontributors.Thefailureinitiatormightbedifferent fromthe failurecontributorthat ultimatelyleadstothedamage propagationandthesystemfailure.Often,whatappearstobethe problemisonlyitsvisiblepart(symptomandeffectofunderlying causes)thatmaynotalsoberepresentativeofthetotalcontentof problem. The failure initiator is mainly triggered by a design problem that remained inherent for a considered system. The

failurecontributorisoftengeneratedbyadrivingpracticethatstays inappropriate for a target system. The statistical analysis can explainthemajorfactorsofafailuresituationandmaybeusedto better understanding the historical description of problems in maintenance for thestudied system. The descriptive statistical analysis ofindustrial rawdata providesinteresting information andpermitsrefinedtherolesofmultipleinvolvedvariableswith the determination of most influential in order to prevent a resurgenceofdamageandfailure.Maintenancecouldbenefitfrom collaborative workshops and resource materials that provide insight into the underlying causes and operational means to discovereffectivewaystopreventthemfromhappeninginthe future. When a strong correlation between underlying causes, failure initiators, failure contributors and failure modes hasbeen establishedbycontrolledstudies,arulecanbedevisedthatrelates some consequence adjustment factors with expert opinions. Rather thanthe classicalelucidation of approaching thefailure problemasacollectionofindependentfactorsthatareeventually linkedtogether,aunifiedmethodologyopensnewpossibilitiesby treatingthesystemfailureprocesswithacomprehensiveview.

Inthecontextofpossiblefailuremodesonrotarymachinery system,themainfailureinitiatorsareelectricalfaultsor malfunc-tions, mechanical breakage, overheating, and other insulation breakdowns. The collected experiences indicatethat thefailure contributors’highvibration,persistentoverloadingandaggressive chemicalsarethemostimportant.Thelessonslearnedshowthat the most frequent underlying causes are defective components, improper operation, and inadequate physical protection. The argumentsoftheexpertknowledgerulesbecomeassociatedwith the focus on formal troubleshooting procedures; preventive maintenance and safety issues revolving around components within the rotary machinery system. In the eight rules, the differentspecialistsapplytroubleshootingstrategies toidentify, localizeand(wherepossible)correctmalfunctions.

5.2. Visualreasoningofmaintenanceanalysisprocedures

The development of such conceptual rule models is to the advantage of conversion of tacit into explicit knowledge. This transformationexpandstheknowledgeacquiredordiscoveredby individualsanditsformalrepresentationaimsatprovidingshared meaningsofmaintenanceanalysisprocedures.Shared interpreta-tionsarevaluabletoimprovecommunicationbetweenexpertsfor strengthening their collective knowledge. After knowledge is acquiredand formalized,itis concretelyexploitedbymeansof knowledgemanagementmethodsandtools[26].Forinstance,in an analogous wayto thequery-based cross-languagediagnosis tool presentedin[62],it ispossibletobuildaquery CG based-languagediagnosistoolforassistingusersdiagnosingequipment defect troubleshooting. The conceptual framework is well-equipped to handle the situation of how users troubleshoot problems by query diagnoses, withan existing ontology-based semanticsearchenginethatimplementssuchamatchingfunction

using the CG reasoning operations (validation and inference services)[63].

Itisimportantfor themaintenance systemtohave compre-hensive information and knowledgeformalization onwhich to basethecollaborationpolicydecisionsandresponsestocomplex problemsituations.Thepragmaticstrategyisintendedtodevelop sufficient awareness and capacity among collaborative actors, communities and organizations so that they can prevent or remediateexistingproblems.

Theconventional techniques(e.g.traditionalCBR) cannotbe usedtosupporttheproblem.Inthatconnection,wewouldliketo stresstwosignificantpoints.

-It was important to gain a clear idea of how knowledge representationinthat<CB>wouldproceed.

Eachexperthasitsown modeof reasoningaccordingtohis experiencesanddomainofcompetence.

Thisis acleardemonstration oftheneedtorely ondomain ontology and a dedicated language such as conceptualgraphs, sinceifeveryoneadoptstheappropriatelevelofformalizationfor cases description; it is possible to achieve formal knowledge representation.

-Thecasebase<CB>willbeprogressivelyandregularlyupdated onthebasisofnewlessonslearnedinthetreatmentofnewcases. Theapplicationofrigorouscontrolandmonitoringprocedures wouldprovide aclear andconstantly updatedviewof <CB>, particularlywithregardtotheunderlyingpolicyobjectivesand industrialevolution.

Ourconceptualgraphmodelingapproachcanberegardedas complementarytotheconventionaltechniquesdevelopment(e.g. CBR),becausegraph-basedcommunicationshouldbeanelement that reinforces the efficiency of knowledge representation by makingitcomprehensibleandfacilitatingitssemanticsharingby collaborative actors. The reasoning operations (derivation, spe-cialization, generalization and projection) of CGs strengthen collaborationbyemphasizingtheprimacy ofknowledgethat is validlyenactedbythecollaborativeactionsofexperts.

Ontheonehand,weconsidernowtheintegrativejoinofthe eightexpertrules inasinglegraphdescribing thecollaborative expertise(asshowninFig.7).

This join has to first find the concepts belonging to the minimum commongeneralization of involved graphs and then onlykeepthosefromtheircommonmaximumspecialization.In particular, inspection of the graphs shows, for example, the possibilityofgeneralizationbetweentheconceptsBearing,Gearbox

and Motorthat are alltheequipmentcomponent of therotary machinerysystem.Similarly,theconceptselectricalmalfunction, mechanicalbreakage,overheating,andinsulationbreakdownare alltypesoffailureinitiators.Wethenexplainedthattheconcepts with similar role (failure initiators, failure contributors and underlyingcauses)intheanalysisofsystemreliabilityaremerged intoonesingleCG.

Ontheotherhand,wecanusethisexperiencedknowledgefor dedicatedanalysisofthestudiedsystem.Forexample,weseekout specificproblemsonMotorwhichwouldjustifysectorialtargeting ofactions.In suchasituation,theprojectionoperationprovides thatthethreealternativepiecesofexperiencedknowledge(rules 4, 5 and 6)can beselectedto establishthat the priorities and measuresthataredirectedatresolvingthemajorproblemofthe

Motor (Fig. 8). Moreover, there is a need for joint efforts on everyone’s part to overcome deplorable state of component equipmentfailuresandtotranslateproactivegainedknowledge intoaction.Expertteamsshoulddiscusswhattheywoulddointhis

kind of problem solving, and settle appropriate plans, so that maintainersaresupportedinanyreliabilityactiontheytakewith CGsreasoningandtheirvisualoperations.Theseoperationstook accountofapreliminarystudyincorporatingastatisticalanalysis bymaintenancecenter,ananalysisofadvanced knowledgeand continuous technological intelligence. Using statistical analysis andvisualizationtoolsonmaintenancedataisnowanintegralpart ofanytechnologicalintelligenceactivityinexperiencefeedback processes.

Thusinordertoassess,interpretandvalidatethe reasonable-ness of themodeled rules, one would have to ensurethat the various types of situations targeted by using the projection operationofCGs.WeareinterestedinCGstoleveragetheirspecific visualcapabilitiesofexpressiverepresentationand communica-tivereasoningwhichallowtheusertoproperlyevaluatetherules

astoidentifythoseheconsidersbeingthemostinterestingwithin a broad analytical framework. In the same vein, we can distinguished two generic types of rules model that can be consideredbytheusertospecifyhiscontextualizedrequirements byexplicitlydescribingtherulesseparatelylabeledasinteresting andnon-interesting(‘‘includetemplate’’and‘‘excludetemplate’’) [64].Thus,eachmodeledruleiscomparedwiththeelementsofthe setsoftwotypesofpredefinedgenericmodels.Aruleissaidtobe consideredsatisfactoryifitisaspecializationofatleastonerule considered interesting (‘‘include template’’) and if it does not match any specialization of rules considered non-interesting (‘‘exclude template’’).Such a classification is alwaysimproving the analysis of modeled rules by more effective interaction to facilitate an agreed interpretation of theresults [65]. The rule selectionmodecanbeeasilytransposedtothereasoningsystemof

Context : rotary machineryfailure

description

Bearing Rotary Machine _Component

2 1 2 1 Implication 2 Component Gearbox Motor Component 1 2 2

Analysis: failureinitiators

description Mechanical breakage 1 OR 2 OR 1 2 Electrical malfunction Insulation Breakdown Overheating OR 1 2 OR 1 2 1

Analysis: failurecontributors

description Persistent Overloading 1 OR 2 OR 1 2 Aggressive Chemicals Poor Lubrication High Vibration OR 1 2 OR 1 2

Analysis: underlyingcauses

description

Defective Components 1 OR 2

OR

1 2

Inadequate Physical Protection

Machine Eccentricity Improper Operation OR 1 2 OR 1 2 Before 1 2 Before 1 Characterization 2 1

Fig.7.Aconceptualgraphruleformalizingthecollaborativeintegrationofexpertrules.

Rotary machineryfailure description Motor implication 1 2 description Insulation Breakdown Failure initiator description Electrical Malfunction OR 2 1 OR 2 1 description ---before 2 1 description High Vibration Failure contributor description Persistent Overloading OR 2 1 OR 2 1 description ---description Improper Operation Underlying causes description Defective Components OR 2 1 OR 2 1 description ---before 2 1

conceptualgraphsinwhichtheprojectionoperationcanbeusedto passjudgmentonthevalidityofothergraphs[54].Thisoperation anditsderivationsareusedtoperformtheruleselectionverifying thespecifiedrequirementsbycheckingthevalidityoftheirgraph with respect to a rule base asserted by graphs. In the above example,itwaspossibletofindareasonablematchingbetween specifiedCGrulesandtargetmonitoredmaintenancetransactions. Thus, the rules selection would have to proceed to informed choices of the preventive or curative actions to be chosen for maintenance.Hence,wecouldmeasuredirectlytheeffectofthe graphical reasoning on actors’ ability to obtain appropriate knowledge in line with actions identified in the collaborative strategyconcerningmaintenancemanagement.Furthermore,the problem shouldnotbeviewedas theproblem ofoneexpertise more than anotherbut as a globalproblem which all involved membersofthecollaborativeorganizationwouldaddress.

5.3. Discussion

Itis alsoimportant tocombine thesymbolicand numerical reasoningincomplementaryways.Inordertoorganizenumerical reasoning, the industrial maintenance framework draws its knowledgefromthewealthofinformationavailablefromexperts, customers, practical research and published documentation on systemperformanceandfailuremodes.Theyconcerninparticular thevariousdatesrelatedtoproducts(commissioning,allfailures withtheplacesatwhichsuchfailuresweremadeand recommis-sioning).Themaintenancejournalrecordsfurnishdetailsforthe extentoftheworkperformedandthecomponentsexchangedand installed. The analysis requires the description of significant assumptionswithrootcausesinvestigationandthepotentialuse ofexternalexpertise.Aloweringofcorrectivemaintenance,and thereforeanimprovementinindustrialperformance,isachieved through experience feedback- and reliability-oriented mainte-nance;unplanneddowntimesarereduced.Someguidelinescanbe usedforplanningareliabilitygrowthtestwithanapplicablemodel (e.g.Duanemodel)andthecontributionofexpertiseenablingusto identifyfailuremodes,andvalidatefactsthathelpusdirectthe focusoftheinvestigation.Theobjectiveistofindfailuresduring testandlearnfromthosefailuresbyimprovingtheconditionsfor the functioning of studiedsystems or redesigning to eliminate them.Anotheranalysiscanbeconductedtoidentifyanddescribe potential failure modes for assessment and inclusion in a surveillance program. The analysis and interpretation of the results are frequently determined using the Weibull method. Further actions can be made to define the key parameters of surveillancetosupportthemaintenanceoperations,buildingon theidentifiedmodesoffailure.Toavoidanyruptureinitslogistical chainwhichmayleadtoastoppageoftheservicesandresources availability,itisuptothemaintenancemanagementtoassessthe appropriatenessoftheconstitutionbyitofapertinentsafetystock. This requires an in-depth investigations and a detailed risk assessment toidentifythemannerby whicha systemmayfail tooperatecorrectly,predictthepotentialconsequencesofsucha failure,andestablishspecificengineeringmeasures(e.g.usinga

Poisson distribution) to mitigate the consequences to tolerable levels[66].Establishingabaselineofexperiencedknowledgeata maintenance level is essential in order to implement the collaboration,asappropriate,inexchangingtimelyandaccurate information concerningtheproblem solvingandits prevention. Maintenance management services benefitfrom more efficient, timely, and accurate collection, interpretation, and analysis of informationwithcorrespondingbenefitsofashortened investiga-tion process and more timely communication of reliability deficienciesandproblemsolvingreportstostakeholdersandthe logistics.

OurworkhassomecommonfeatureswiththeDecisionalDNA’s approach[67,68]:(i)theSetofExperienceKnowledgeStructure, usedtomodeltheuser’sexperienceand (ii)theexploitation of embedded knowledgeinthedomainofindustrial maintenance. However,thisapproachhasarestriction:somephysicalmodels (functions) are needed to describe knowledge experience of a specificdomain.

6. Conclusionandrelatedworks

In this paper, we have shown that the use of cognitive experience feedback and formal semantic techniques provide additionalsupporttomaintenance tasksby improvingtheuser understandingofthecomponentsbeingmaintained.Thecreation and exploitation of collaborative knowledge of experts in the courseofhandlingmaintenanceproblemresolutionyieldrelevant knowledge which is accessible to them based on their needs. Relyingonthemodelingofexpertknowledgethroughconceptual graphs operations, we proposed an approach that positively impactsthemaintenancemanagementplans:

a better access to the lessons acquired from the experience feedbackprocessthroughintelligentinformationretrievalbased onaformalizeddomainvocabulary;

aframeworkforexpertsknowledgesharingthatpromotesthe crossingofpointsofviews.Theyprovideadditionalsupportto tolerateorpreventfailuresandoptimizeequipmentavailability [69];

a possible conceptual model of a maintenance management systemthatcombinesknowledge,userexperienceandsemantic techniques.

Theoutlookintheshorttermofthisworkis:

adescriptionofmeta-rulesforthestudyoftheconsistencyofa setofexpertrules;

theresearchofconflictresolutiontechniques(negotiationlogic ordeterminingprioritiesforinterpretation);

theconsiderationofchangingtheriskmanagementmechanisms anddecision-makinginconflictsituations.

Forthemediumterm,wewishtointroduceamajoridea:the descriptionofawayofcapitalizationofthetraceofreasoningafter acollaborativeproblemsolving.Amongsttheactionswhichcanbe applied, a range of semantic procedures can help to monitor collaborativeoperationsandtrackproblemsolvingfrompointof initiation,throughprocessingnegotiations,changesandontofinal resolution.

The expected added value is the proposal of experience feedback system on management of collaborative decision schemes achieving a knowledge capitalization method that allowsunderstandingthewayinwhichcollaborativereasoning is efficiently conducted through appropriate choices [70]. In particular, the instrument of the collaborative CBR, with its distributed and teamwork mechanisms, can be managed to ensurethattheproblemsolvingprocessiswelldirectedandis determinedingoodtime.

Moreover,thepossiblebenefitistofuturecomplexsituations withthecollectiveknowledgethroughagatheringofcollaborative practices. This requires developing methods and techniquesto promotesuitableknowledgetrackingsystems;thekeypointisto build up a sustainable and semantically formal reasoning framework that allows tracking of actions taken and decisions made[71].

Similarly, thisworkshould beconcernedwithtwo forms of long-termextension: