SOFC modelling based on discrete Bayesian network for system diagnosis use

K. Wang, M.C. Pera, D. Hissel, N. Yousfi Steiner, Antti Pohjoranta, S. Pofahl

    Research output: Contribution to journalArticle in a proceedings journalScientificpeer-review

    1 Citation (Scopus)

    Abstract

    We propose in this paper a diagnosis method that is aimed to detect and isolate SOFC system fault by using the FC stack as a sensor. A discrete Bayesian network (BN) was established to illustrate the input-output causal relations of the stack. In order to examine the generalizability of the network structure, the BN was parameterized to fit the experimental data from two different SOFC systems. The models showed reasonable accuracy of state estimation for 6 operating variables. Finally, the BN model was experimented for diagnosing a specified system fault.
    Original languageEnglish
    Pages (from-to)675-680
    JournalIFAC Proceedings Volumes
    Volume45
    Issue number21
    DOIs
    Publication statusPublished - 2012
    MoE publication typeA4 Article in a conference publication
    Event8th Power Plant and Power System Control Symposium, PPPSC 2012 - Toulouse, France
    Duration: 2 Sep 20125 Sep 2012

    Fingerprint

    Bayesian networks
    Solid oxide fuel cells (SOFC)
    State estimation
    Sensors

    Keywords

    • Bayesian network
    • conditional probability
    • fault diagnosis
    • maximum likelihood
    • SOFC

    Cite this

    Wang, K., Pera, M. C., Hissel, D., Yousfi Steiner, N., Pohjoranta, A., & Pofahl, S. (2012). SOFC modelling based on discrete Bayesian network for system diagnosis use. IFAC Proceedings Volumes, 45(21), 675-680. https://doi.org/10.3182/20120902-4-FR-2032.00118
    Wang, K. ; Pera, M.C. ; Hissel, D. ; Yousfi Steiner, N. ; Pohjoranta, Antti ; Pofahl, S. / SOFC modelling based on discrete Bayesian network for system diagnosis use. In: IFAC Proceedings Volumes. 2012 ; Vol. 45, No. 21. pp. 675-680.
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    abstract = "We propose in this paper a diagnosis method that is aimed to detect and isolate SOFC system fault by using the FC stack as a sensor. A discrete Bayesian network (BN) was established to illustrate the input-output causal relations of the stack. In order to examine the generalizability of the network structure, the BN was parameterized to fit the experimental data from two different SOFC systems. The models showed reasonable accuracy of state estimation for 6 operating variables. Finally, the BN model was experimented for diagnosing a specified system fault.",
    keywords = "Bayesian network, conditional probability, fault diagnosis, maximum likelihood, SOFC",
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    Wang, K, Pera, MC, Hissel, D, Yousfi Steiner, N, Pohjoranta, A & Pofahl, S 2012, 'SOFC modelling based on discrete Bayesian network for system diagnosis use', IFAC Proceedings Volumes, vol. 45, no. 21, pp. 675-680. https://doi.org/10.3182/20120902-4-FR-2032.00118

    SOFC modelling based on discrete Bayesian network for system diagnosis use. / Wang, K.; Pera, M.C.; Hissel, D.; Yousfi Steiner, N.; Pohjoranta, Antti; Pofahl, S.

    In: IFAC Proceedings Volumes, Vol. 45, No. 21, 2012, p. 675-680.

    Research output: Contribution to journalArticle in a proceedings journalScientificpeer-review

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    KW - conditional probability

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    KW - maximum likelihood

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