IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-642-37270-4_55.html
   My bibliography  Save this book chapter

Data-Driven Based Key Performance Index Residual Generation and Its Application on Complex Electrical Equipment

In: The 19th International Conference on Industrial Engineering and Engineering Management

Author

Listed:
  • Zhi-gang Yao

    (Shijiazhuang Mechanical Engineering College)

  • Li Cheng

    (Lishui University)

  • Yu-lei Wang

    (University of Duisburg-Essen)

Abstract

Motivated by the increasing needs for key performance index related fault detection in complex electrical equipments, this paper proposes the subspace aided data-driven robust fault detection technique. The main idea is to use the original test data to identify the residual generators firstly, and then make use of performance indices to design of robust residuals which are robustness to non-quality variables and sensitivity to quality variables. Robust and robust reduced order residual generations are proposed, and finally the proposed methods are certified by application on complex electrical equipment.

Suggested Citation

  • Zhi-gang Yao & Li Cheng & Yu-lei Wang, 2013. "Data-Driven Based Key Performance Index Residual Generation and Its Application on Complex Electrical Equipment," Springer Books, in: Ershi Qi & Jiang Shen & Runliang Dou (ed.), The 19th International Conference on Industrial Engineering and Engineering Management, edition 127, chapter 0, pages 577-586, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-37270-4_55
    DOI: 10.1007/978-3-642-37270-4_55
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:sprchp:978-3-642-37270-4_55. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.