IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i20p6811-d659192.html
   My bibliography  Save this article

Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems

Author

Listed:
  • Len Gelman

    (Department of Engineering and Technology, School of Computing and Engineering, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK)

  • Krzysztof Soliński

    (Meggitt Sensing Systems, Rte de Moncor 4, 1701 Fribourg, Switzerland)

  • Andrew Ball

    (Department of Engineering and Technology, School of Computing and Engineering, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK)

Abstract

Higher order spectra exhibit a powerful detection capability of low-energy fault-related signal components, buried in background random noise. This paper investigates the powerful nonlinear non-stationary instantaneous wavelet bicoherence for local gear fault detection. The new methodology of selecting frequency bands that are relevant for wavelet bicoherence fault detection is proposed and investigated. The capabilities of wavelet bicoherence are proven for early-stage fault detection in a gear pinion, in which natural pitting has developed in multiple pinion teeth in the course of endurance gearbox tests. The results of the WB-based fault detection are compared with a stereo optical fault evaluation. The reliability of WB-based fault detection is quantified based on the complete probability of correct identification. This paper is the first attempt to investigate instantaneous wavelet bicoherence technology for the detection of multiple natural early-stage local gear faults, based on comprehensive statistical evaluation of the industrially relevant detection effectiveness estimate—the complete probability of correct fault detection.

Suggested Citation

  • Len Gelman & Krzysztof Soliński & Andrew Ball, 2021. "Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems," Energies, MDPI, vol. 14(20), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6811-:d:659192
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/20/6811/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/20/6811/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hui Li & Bangji Fan & Rong Jia & Fang Zhai & Liang Bai & Xingqi Luo, 2020. "Research on Multi-Domain Fault Diagnosis of Gearbox of Wind Turbine Based on Adaptive Variational Mode Decomposition and Extreme Learning Machine Algorithms," Energies, MDPI, vol. 13(6), pages 1-20, March.
    Full references (including those not matched with items on IDEAS)

    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.
    1. Fangqin Zhang & Yan Kang & Xiao Cheng & Peiru Chen & Songbai Song, 2022. "A Hybrid Model Integrating Elman Neural Network with Variational Mode Decomposition and Box–Cox Transformation for Monthly Runoff Time Series Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3673-3697, August.
    2. Acarer, Sercan & Uyulan, Çağlar & Karadeniz, Ziya Haktan, 2020. "Optimization of radial inflow wind turbines for urban wind energy harvesting," Energy, Elsevier, vol. 202(C).
    3. Jersson X. Leon-Medina & Francesc Pozo, 2023. "Moving towards Preventive Maintenance in Wind Turbine Structural Control and Health Monitoring," Energies, MDPI, vol. 16(6), pages 1-4, March.
    4. Hui Li & Fan Li & Rong Jia & Fang Zhai & Liang Bai & Xingqi Luo, 2021. "Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework," Energies, MDPI, vol. 14(6), pages 1-19, March.

    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:20:p:6811-:d:659192. 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.