IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v52y2021i14p3035-3043.html
   My bibliography  Save this article

Incipient fault prediction based on generalised correntropy filtering for non-Gaussian stochastic systems

Author

Listed:
  • Lifan Li
  • Lina Yao

Abstract

In this paper, the problem of incipient fault prediction is studied for the nonlinear stochastic system with non-Gaussian noises and actuator fault. The incipient fault is expressed as a nonlinear function with two unknown parameters (the occurring time of fault and the incipient fault evolution rate). Based on the generalised correntropy criterion, the fault detection filter is proposed, and then the occurring time of fault can be obtained. Once the fault is detected, the unknown fault evolution rate is estimated by designing a new generalised correntropy filter-based. According to the estimated fault occurrence time and the estimated fault evolution rate, the trend of incipient fault can be predicted. Finally, the simulation results of a single-link robotic flexible manipulator system are given to show that the proposed method is validated.

Suggested Citation

  • Lifan Li & Lina Yao, 2021. "Incipient fault prediction based on generalised correntropy filtering for non-Gaussian stochastic systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(14), pages 3035-3043, October.
  • Handle: RePEc:taf:tsysxx:v:52:y:2021:i:14:p:3035-3043
    DOI: 10.1080/00207721.2021.1918281
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2021.1918281
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207721.2021.1918281?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:taf:tsysxx:v:52:y:2021:i:14:p:3035-3043. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TSYS20 .

    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.