IDEAS home Printed from https://ideas.repec.org/a/spr/advdac/v8y2014i4p377-401.html
   My bibliography  Save this article

Feature selection for fault level diagnosis of planetary gearboxes

Author

Listed:
  • Zhiliang Liu
  • Xiaomin Zhao
  • Ming Zuo
  • Hongbing Xu

Abstract

Feature selection is critical to maintain high performance of classification-based fault diagnosis with a large feature size. In this paper, we propose a criterion to evaluate features effectiveness by class separability that is defined on cosine similarity in the kernel space of the Gaussian radial basis function. We develop a feature selection algorithm accordingly using the proposed criterion together with sequential backward selection and a feature re-ranking mechanism. We then employ the proposed feature selection algorithm to determine fault-sensitive features and select them for fault level diagnosis of planetary gearboxes. The experimental results demonstrate that the proposed algorithm can effectively reduce the feature size and improve accuracy of fault level diagnosis simultaneously. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Zhiliang Liu & Xiaomin Zhao & Ming Zuo & Hongbing Xu, 2014. "Feature selection for fault level diagnosis of planetary gearboxes," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(4), pages 377-401, December.
    • Handle: RePEc:spr:advdac:v:8:y:2014:i:4:p:377-401
    DOI: 10.1007/s11634-014-0168-4
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11634-014-0168-4
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11634-014-0168-4?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.

    Citations

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


    Cited by:

    1. E. Emary & Hossam M. Zawbaa & Aboul Ella Hassanien & B. Parv, 2017. "Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 611-627, September.
    2. Asma Gul & Aris Perperoglou & Zardad Khan & Osama Mahmoud & Miftahuddin Miftahuddin & Werner Adler & Berthold Lausen, 2018. "Ensemble of a subset of kNN classifiers," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(4), pages 827-840, December.

    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:advdac:v:8:y:2014:i:4:p:377-401. 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.