An improved Autogram and MOMEDA method to detect weak compound fault in rolling bearings

Xuyang Xie; Zichun Yang; Lei Zhang; Guoqing Zeng; Xuefeng Wang; Peng Zhang; Guobing Chen; Xuyang Xie; Zichun Yang; Lei Zhang; Guoqing Zeng; Xuefeng Wang; Peng Zhang; Guobing Chen

doi:10.3934/mbe.2022488

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 10: 10424-10444. doi: 10.3934/mbe.2022488

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Research article

An improved Autogram and MOMEDA method to detect weak compound fault in rolling bearings

College of Power Engineering, Naval University of Engineering, Wuhan 430033, China

Academic Editor: Runzhang Xu

Received: 21 May 2022 Revised: 11 July 2022 Accepted: 13 July 2022 Published: 22 July 2022

When weak compound fault occurs in rolling bearing, the faint fault features suffer from serious noise interference, and different type faults are coupled together, making it a great challenge to separate the fault features. To solve the problems, a novel weak compound fault diagnosis method for rolling bearing based on improved Autogram and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed. Firstly, the kurtosis index in Autogram is modified with multi-scale permutation entropy, and improved Autogram finds the optimal resonance frequency band to preliminarily denoise the weak compound fault signal. Then, MOMEDA is performed to deconvolute the denoised signal to decouple the features of compound fault. Finally, square envelope analysis is applied on the separated deconvoluted signals to identify different type faults according to the fault characteristic frequencies in the spectrums. The proposed method is performed to analyze the simulated signal and experimental datasets of different types of rolling bearing weak compound faults. The results indicate that the proposed method can accurately diagnose the weak compound faults, and comparison with the analysis results of parameter-adaptive variational mode decomposition algorithm verifies its effectiveness and superiority.
- compound fault diagnosis,
- rolling bearing,
- Autogram,
- multipoint optimal minimum entropy deconvolution adjusted
Citation: Xuyang Xie, Zichun Yang, Lei Zhang, Guoqing Zeng, Xuefeng Wang, Peng Zhang, Guobing Chen. An improved Autogram and MOMEDA method to detect weak compound fault in rolling bearings[J]. Mathematical Biosciences and Engineering, 2022, 19(10): 10424-10444. doi: 10.3934/mbe.2022488

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Abstract

When weak compound fault occurs in rolling bearing, the faint fault features suffer from serious noise interference, and different type faults are coupled together, making it a great challenge to separate the fault features. To solve the problems, a novel weak compound fault diagnosis method for rolling bearing based on improved Autogram and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed. Firstly, the kurtosis index in Autogram is modified with multi-scale permutation entropy, and improved Autogram finds the optimal resonance frequency band to preliminarily denoise the weak compound fault signal. Then, MOMEDA is performed to deconvolute the denoised signal to decouple the features of compound fault. Finally, square envelope analysis is applied on the separated deconvoluted signals to identify different type faults according to the fault characteristic frequencies in the spectrums. The proposed method is performed to analyze the simulated signal and experimental datasets of different types of rolling bearing weak compound faults. The results indicate that the proposed method can accurately diagnose the weak compound faults, and comparison with the analysis results of parameter-adaptive variational mode decomposition algorithm verifies its effectiveness and superiority.

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