IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v30y2019i1d10.1007_s10845-016-1243-9.html
   My bibliography  Save this article

A supervised sparsity-based wavelet feature for bearing fault diagnosis

Author

Listed:
  • Cong Wang

    (University of Science and Technology of China (USTC))

  • Meng Gan

    (University of Science and Technology of China (USTC))

  • Chang’an Zhu

    (University of Science and Technology of China (USTC))

Abstract

This paper proposes a supervised sparsity-based wavelet feature (SSWF) for the detection of bearing fault, which combines wavelet packet transform (WPT) and sparse coding. SSWF is extracted from vibration signals by four main steps: (1) construct a WPT vector using the fault-related WPT coefficients; (2) design a structured dictionary that combines the signal characteristics and class information; (3) use the dictionary to implement the sparse coding of the WPT vectors, which can be solved by basis pursuit (BP) and (4) calculate the SSWF from the sparse coefficients. During the process, WPT can detect the fault occurrence of the bearing signal. Sparse coding based on a structured dictionary can find a robust representation of the signal and at the same time, integrate the class information. Therefore, SSWF is able to stably and discriminatively reflect different fault types, which indicates its potential in bearing fault diagnosis. Experiments on two bearing cases are conducted to verify the advantages of SSWF in the detection of bearing faults.

Suggested Citation

  • Cong Wang & Meng Gan & Chang’an Zhu, 2019. "A supervised sparsity-based wavelet feature for bearing fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 229-239, January.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1243-9
    DOI: 10.1007/s10845-016-1243-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-016-1243-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-016-1243-9?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. Li, Jimeng & Cheng, Xing & Peng, Junling & Meng, Zong, 2022. "A new adaptive parallel resonance system based on cascaded feedback model of vibrational resonance and stochastic resonance and its application in fault detection of rolling bearings," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    2. Jialin Li & Xueyi Li & David He & Yongzhi Qu, 2020. "Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1899-1916, December.
    3. Xiaoyin Nie & Gang Xie, 2021. "A novel normalized recurrent neural network for fault diagnosis with noisy labels," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1271-1288, June.
    4. Dengyu Xiao & Chengjin Qin & Honggan Yu & Yixiang Huang & Chengliang Liu, 2021. "Unsupervised deep representation learning for motor fault diagnosis by mutual information maximization," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 377-391, February.

    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:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1243-9. 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.