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: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-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
    Title of host publication8th Power Plant and Power System Control Symposium
    EditorsMaurice Fadel, Stéphane Caux
    PublisherElsevier
    Pages675-680
    ISBN (Print)978-3-902823-24-3
    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

    Publication series

    SeriesIFAC Proceedings Volumes
    Number21
    Volume45
    ISSN1474-6670

    Conference

    Conference8th Power Plant and Power System Control Symposium, PPPSC 2012
    Abbreviated titlePPPSC 2012
    CountryFrance
    CityToulouse
    Period2/09/125/09/12

    Keywords

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

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  • 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. In M. Fadel, & S. Caux (Eds.), 8th Power Plant and Power System Control Symposium (pp. 675-680). Elsevier. IFAC Proceedings Volumes, No. 21, Vol.. 45 https://doi.org/10.3182/20120902-4-FR-2032.00118