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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
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    References listed on IDEAS

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    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.
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