IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/282547.html
   My bibliography  Save this article

Joint Feature and Model Selection for SVM Fault Diagnosis in Solid Oxide Fuel Cell Systems

Author

Listed:
  • Gabriele Moser
  • Paola Costamagna
  • Andrea De Giorgi
  • Andrea Greco
  • Loredana Magistri
  • Lissy Pellaco
  • Andrea Trucco

Abstract

This paper describes an original technique for the joint feature and model selection in the context of support vector machine (SVM) classification applied as a diagnosis strategy in model-based fault detection and isolation (FDI). We demonstrate that the proposed technique contributes to the solution of an open research problem: to design a robust FDI procedure, correctly functioning with different operating conditions and fault sizes, specifically settled for an electric generation system based on solid oxide fuel cells (SOFCs). By using a quantitative model of the generation system coupled to an optimized SVM classifier, a satisfactory FDI procedure is achieved, which is robust against modeling and measurement errors and is compliant with practical deployment.

Suggested Citation

  • Gabriele Moser & Paola Costamagna & Andrea De Giorgi & Andrea Greco & Loredana Magistri & Lissy Pellaco & Andrea Trucco, 2015. "Joint Feature and Model Selection for SVM Fault Diagnosis in Solid Oxide Fuel Cell Systems," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-12, May.
    • Handle: RePEc:hin:jnlmpe:282547
    DOI: 10.1155/2015/282547
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2015/282547.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2015/282547.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2015/282547?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mingfei Li & Zhengpeng Chen & Jiangbo Dong & Kai Xiong & Chuangting Chen & Mumin Rao & Zhiping Peng & Xi Li & Jingxuan Peng, 2022. "A Data-Driven Fault Diagnosis Method for Solid Oxide Fuel Cell Systems," Energies, MDPI, vol. 15(7), pages 1-16, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:282547. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.