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ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

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General Research Article

Research on Typical Fault Diagnosis of Gear based on GHM Multiwavelet Transform and MCKD Algorithm

Author(s): Hongxin Zhang*, Zhongshu Ji and Ruijin Song

Volume 18, Issue 7, 2024

Published on: 13 September, 2023

Article ID: e210723219004 Pages: 11

DOI: 10.2174/1872212118666230721153234

Price: $65

Abstract

Background: Gear is the key part of the transmission part of mechanical equipment, which has a high failure rate under complex working conditions or long-term operation, directly affecting the reliability of mechanical equipment. To realize online monitoring of gears and predict gear faults, whether the diagnosis of typical gear faults is accurate and timely is a key link, so it is necessary to study the diagnosis of typical gear faults.

Objective: This paper aims to propose a method that can effectively diagnose typical faults of gears and is easy to program, which is beneficial to realize online monitoring of rotating machinery equipment faults.

Method: A typical gear fault diagnosis method based on GHM multiwavelet transform and maximum correlation kurtosis deconvolution algorithm (MCKD) is proposed in this paper. The measured vibration signal is decomposed into components with multiple scale spaces and frequency bands through the GHM multiwavelet transform. Then, the MCKD algorithm is run to suppress the noise of GHM multiwavelet transform coefficients, and the periodic components containing fault information are retained and reconstructed. The typical faults such as gear pitting and broken teeth are tested.

Results: GHM multiwavelet transform combined with MCKD algorithm can accurately locate the frequency band containing fault information from the vibration signal with multiple modulations, double sideband, and multi-frequency interference and effectively extract the fault frequency and its frequency multiplication. The root means a square error of the extracted fault frequency is 0.08. This method has a good noise suppression effect and high accuracy in extracting fault frequency.

Conclusion: The method proposed in this paper is helpful in realizing automatic programming of gear fault diagnosis and is of great significance in realizing real-time monitoring and online diagnosis of rotating machinery equipment faults. More related patents will appear in the future.

Graphical Abstract

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