Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems

Len Gelman, Krzysztof Soliński and Andrew Ball
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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

Energies, 2021, vol. 14, issue 20, 1-18

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.

Keywords: condition monitoring; fault detection; vibration analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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