On Structural Properties to Improve FMEA-Based Abductive Diagnosis

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Abstract

Abductive Model-Based Diagnosis (MBD) provides
an intuitive approach to fault identification by
reasoning on a description of the system to be diagnosed.
Nevertheless, its computational complexity
hinders a vast adoption and thus motivates further
evaluation of efficient methods. In this paper, we
investigate the structural metrics inherent to models
and diagnosis problems generated on the basis of
Failure Mode Effect Analysis (FMEA). Proceeding
on the metrics developed, we investigate their potential
as classification features to identify the most
suitable diagnosis algorithm for a particular diagnosis
problem. Evaluated on artificial and practical
samples, our approach shows that the classifier
trained on the described metrics is able to indicate
the most efficient method in case of a specific diagnosis
scenario
Originalspracheenglisch
TitelProceedings of the Workshop on Knowledge-based Techniques for Problem Solving and Reasoning
ErscheinungsortNew York City, USA
Herausgeber (Verlag)CEUR WS Proceedings
Seitenumfang7
BandVol-1648
PublikationsstatusVeröffentlicht - 10 Jul 2016

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Structural properties

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    Koitz, R., & Wotawa, F. (2016). On Structural Properties to Improve FMEA-Based Abductive Diagnosis. in Proceedings of the Workshop on Knowledge-based Techniques for Problem Solving and Reasoning (Band Vol-1648). New York City, USA: CEUR WS Proceedings.

    On Structural Properties to Improve FMEA-Based Abductive Diagnosis. / Koitz, Roxane; Wotawa, Franz.

    Proceedings of the Workshop on Knowledge-based Techniques for Problem Solving and Reasoning. Band Vol-1648 New York City, USA : CEUR WS Proceedings, 2016.

    Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

    Koitz, R & Wotawa, F 2016, On Structural Properties to Improve FMEA-Based Abductive Diagnosis. in Proceedings of the Workshop on Knowledge-based Techniques for Problem Solving and Reasoning. Bd. Vol-1648, CEUR WS Proceedings, New York City, USA.
    Koitz R, Wotawa F. On Structural Properties to Improve FMEA-Based Abductive Diagnosis. in Proceedings of the Workshop on Knowledge-based Techniques for Problem Solving and Reasoning. Band Vol-1648. New York City, USA: CEUR WS Proceedings. 2016
    Koitz, Roxane ; Wotawa, Franz. / On Structural Properties to Improve FMEA-Based Abductive Diagnosis. Proceedings of the Workshop on Knowledge-based Techniques for Problem Solving and Reasoning. Band Vol-1648 New York City, USA : CEUR WS Proceedings, 2016.
    @inproceedings{ce3a54c5cbd340efa6279cc82d699ff4,
    title = "On Structural Properties to Improve FMEA-Based Abductive Diagnosis",
    abstract = "Abductive Model-Based Diagnosis (MBD) providesan intuitive approach to fault identification byreasoning on a description of the system to be diagnosed.Nevertheless, its computational complexityhinders a vast adoption and thus motivates furtherevaluation of efficient methods. In this paper, weinvestigate the structural metrics inherent to modelsand diagnosis problems generated on the basis ofFailure Mode Effect Analysis (FMEA). Proceedingon the metrics developed, we investigate their potentialas classification features to identify the mostsuitable diagnosis algorithm for a particular diagnosisproblem. Evaluated on artificial and practicalsamples, our approach shows that the classifiertrained on the described metrics is able to indicatethe most efficient method in case of a specific diagnosisscenario",
    keywords = "Abductive Diagnosis, Model-based Diagnosis, Abductive Reasoning, Algorithm Selection",
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    AU - Wotawa, Franz

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    N2 - Abductive Model-Based Diagnosis (MBD) providesan intuitive approach to fault identification byreasoning on a description of the system to be diagnosed.Nevertheless, its computational complexityhinders a vast adoption and thus motivates furtherevaluation of efficient methods. In this paper, weinvestigate the structural metrics inherent to modelsand diagnosis problems generated on the basis ofFailure Mode Effect Analysis (FMEA). Proceedingon the metrics developed, we investigate their potentialas classification features to identify the mostsuitable diagnosis algorithm for a particular diagnosisproblem. Evaluated on artificial and practicalsamples, our approach shows that the classifiertrained on the described metrics is able to indicatethe most efficient method in case of a specific diagnosisscenario

    AB - Abductive Model-Based Diagnosis (MBD) providesan intuitive approach to fault identification byreasoning on a description of the system to be diagnosed.Nevertheless, its computational complexityhinders a vast adoption and thus motivates furtherevaluation of efficient methods. In this paper, weinvestigate the structural metrics inherent to modelsand diagnosis problems generated on the basis ofFailure Mode Effect Analysis (FMEA). Proceedingon the metrics developed, we investigate their potentialas classification features to identify the mostsuitable diagnosis algorithm for a particular diagnosisproblem. Evaluated on artificial and practicalsamples, our approach shows that the classifiertrained on the described metrics is able to indicatethe most efficient method in case of a specific diagnosisscenario

    KW - Abductive Diagnosis

    KW - Model-based Diagnosis

    KW - Abductive Reasoning

    KW - Algorithm Selection

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