Improved Spectral Amplitude Modulation Based on Sparse Feature Adaptive Convolution for Variable Speed Fault Diagnosis of Bearing

Jiawei Lin; Changkun Han; Wei Lu; Liuyang Song; Peng Chen; Huaqing Wang

doi:10.37965/jdmd.2025.706

Authors

Jiawei Lin College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Changkun Han College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China https://orcid.org/0000-0001-6796-163X
Wei Lu College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China; Institute of Engineering Technology, Sinopec Catalyst Company Limited, Beijing 100029, China
Liuyang Song College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China https://orcid.org/0000-0003-4297-1668
Peng Chen Graduate School of Environmental Science and Technology, Mie University, Tsu 514-8507, Japan
Huaqing Wang College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Health Monitoring and Self Recovery for High-end Mechanical Equipment, Beijing University of Chemical Technology, Beijing 100029, China https://orcid.org/0000-0001-5333-0829

DOI:

https://doi.org/10.37965/jdmd.2025.706

Keywords:

bearing fault diagnosis, variable speed, sparse representation, spectral amplitude modulation, feature enhancement

Abstract

Difficulty in extracting nonlinear sparse impulse features due to variable speed conditions and redundant noise interference leads to challenges in diagnosing variable speed faults. Therefore, an improved spectral amplitude modulation based on sparse feature adaptive convolution (ISAM-SFAC) is proposed to enhance the fault features under variable speed condition. First, an optimal bi-damped wavelet construction method is proposed to learn signal impulse features, which selects the optimal bi-damped wavelet parameters with correlation criterion and particle swarm optimization (PSO). Second, a convolutional basis pursuit denoising model based on optimal bi-damped wavelet is proposed for resolving sparse impulses. A model regularization parameter selection method based on weighted fault characteristic amplitude ratio (WFCAR) assistance is proposed. Then, an improved spectral amplitude modulation method based on kurtosis threshold is proposed to further enhance the fault information of sparse signal. Finally, the type of variable speed faults is determined by order spectrum analysis. Various experimental results, such as spectral amplitude modulation and Morlet wavelet matching, verify the effectiveness and advantages of the ISAM-SFAC method.

Conflict of Interest Statement

The authors declare no conflicts of interest.