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UsingModelica ProgramsforDerivingPropositionalHornClause

AbductionProblems

BernhardPeischl,IngoPillandFranzWotawa

{bpeischl,ipill,wotawa}@ist.tugraz.at

ResearchcarriedoutaspartoftheAppliedModelBasedReasoning(AMOR)undergrant842407

Observations

• Idea behind reasoning from first principlesrather old (1980s)

• Some applications (mainly prototypes)– Cars–Webservices&software– Spaceprobes– ....

• WHYISMBRNOT(SOOFTEN)USED?

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Obervations (cont)

• There might be many answers:–Modelingis notthat easy

– Thecurrent engineering and maintenance processis notthat compatible

– Technicalissues (performance,runtime,spacecomplexity,...)

– ...

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SUPPORTMODELING(TOOLS,LANGUAGES,..)

CHANGETHEPROCESSES

NOTTHATPROBLEMATICANYMORE!

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Inthispaper

• Focusonmodelingfordiagnosis!– Useavailablemodels(i.e.fromModelica)– Usefaultmodels– Comparefaultybehaviorwithcorrectbehaviorforobtainingmodels

• Focusonabductive diagnosis

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PropositionalHornClauseAbductionProblem(PHCAP)[Friedrich,Gottlob andNejdl,1990]• Knowledgebase(KB)

• A (propositionalvariables),• (hypotheses),• Th (Hornclauses)

• PHCAP• KB,• (observations)

• Diagnosis()• ,

• MinimaldiagnosisKI2016,F.Wotawa 5

THEAPPROACH

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Modelica

• OOModelingLanguageforCyber-PhysicalSystems(CPSs)

• Numericalsimulation• Multi-purposemodeling• Multi-domainmodeling

• Notappropriatedforbeingusedfordiagnosisdirectly!

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Modelica fordiagnosis

• NeedadifferentmethodtomakeModelicaaccessiblefordiagnosis!

• Conversionapproach• Makeuseoffaultmodels• Learnfromsimulatedbehavior

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Example

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Firststep– qualitativerepresentations

• Insteadofusingquantities,e.g.,8Vor4V,useaqualitativerepresentation– Absolutequantities:

• E.g.8VrepresentedasLARGE– Deviations:

• E.g.Ifweexpect8Vbutmeasure0V,werepresentthisdeviationasSMALLER

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DeviationmodelsbasedonModelica

• Simulateorginal program• Simulateprogramwithintroducedfault(atacertainpointintime)

• Comparethedifferences,andobtainatable,e.g.:

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Secondstep– generaterules• Foreachmodeanddeviationgeneratearule,e.g.:– empty(BAT)® smaller(v1)– empty(BAT)® smaller(v2)

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Example(cont.)– thePHCAPmodel

• A ={empty(BAT),short(R1),broken(R1),short(R2),broken(R2),smaller(v1),larger(v1),smaller(v2),larger(v2)}

• Hyp ={empty(BAT),short(R1),broken(R1),short(R2),broken(R2)}

• Th ={

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Diagnosis

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ok(v1)

ok(v2)

smaller(v1),smaller(v2)

StartdiagnosiswithPHCAPandOBS ={smaller(v1),smaller(v2)}

Diagnoses:{empty(BAT)}

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Conclusions

• CoupleModelica programswithabductivediagnosis

• Obtaindeviationmodelsformacomparisonbetweenthecorrectbehaviorandthebehaviorincaseofafault

• Challenges:– Howtocomparetheoutcome?– Howtointegratetheapproachincaseoflargefiniteautomata?

– Automationofmodelextraction

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Thankyouforyourattention!

QUESTIONS?

ResearchcarriedoutaspartoftheAppliedModelBasedReasoning(AMOR)undergrant842407