IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i8p2322-d539661.html
   My bibliography  Save this article

A Novel Feature Extraction Method for the Condition Monitoring of Bearings

Author

Listed:
  • Abdenour Soualhi

    (Laboratory LASPI EA-3059, University of Jean Monnet, 42100 Saint Etienne, France)

  • Bilal El Yousfi

    (Laboratory LASPI EA-3059, University of Jean Monnet, 42100 Saint Etienne, France)

  • Hubert Razik

    (Laboratory Ampère UMR 5005, University of Lyon, 69007 Lyon, France
    Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China)

  • Tianzhen Wang

    (Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China)

Abstract

This paper presents an innovative approach to the extraction of an indicator for the monitoring of bearing degradation. This approach is based on the principles of the empirical mode decomposition (EMD) and the Hilbert transform (HT). The proposed approach extracts the temporal components of oscillating vibration signals called intrinsic mode functions (IMFs). These components are classified locally from the highest frequencies to the lowest frequencies. By selecting the appropriate components, it is possible to construct a bank of self-adaptive and automatic filters. Combined with the HT, the EMD allows an estimate of the instantaneous frequency of each IMF. A health indicator called the Hilbert marginal spectrum density is then extracted in order to detect and diagnose the degradation of bearings. This approach was validated on two test benches with variable speeds and loads. The obtained results demonstrated the effectiveness of this approach for the monitoring of ball and roller bearings.

Suggested Citation

  • Abdenour Soualhi & Bilal El Yousfi & Hubert Razik & Tianzhen Wang, 2021. "A Novel Feature Extraction Method for the Condition Monitoring of Bearings," Energies, MDPI, vol. 14(8), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2322-:d:539661
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/8/2322/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/8/2322/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jiejunyi Liang & Jian-Hua Zhong & Zhi-Xin Yang, 2017. "Correlated EEMD and Effective Feature Extraction for Both Periodic and Irregular Faults Diagnosis in Rotating Machinery," Energies, MDPI, vol. 10(10), pages 1-14, October.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Xinyue Liu & Yan Yan & Kaibo Hu & Shan Zhang & Hongjie Li & Zhen Zhang & Tingna Shi, 2022. "Fault Diagnosis of Rotor Broken Bar in Induction Motor Based on Successive Variational Mode Decomposition," Energies, MDPI, vol. 15(3), pages 1-16, February.
    2. Shu Han & Xiaoming Liu & Yan Yang & Hailin Cao & Yuanhong Zhong & Chuanlian Luo, 2021. "Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals," Energies, MDPI, vol. 14(22), pages 1-13, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      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:gam:jeners:v:14:y:2021:i:8:p:2322-:d:539661. 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.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.