Fast Sparsity-Assisted Signal Decomposition With Nonconvex Enhancement for Bearing Fault Diagnosis

Sparsity-assisted signal decomposition (SASD) based on morphological component analysis (MCA) for bearing fault diagnosis has been studied in-depth. However, existing algorithms often use different combinations of representation dictionaries and priors, leading to difficult dictionary choice and hig...

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Bibliographic Details
Published in:IEEE/ASME transactions on mechatronics Vol. 27; no. 4; pp. 2333 - 2344
Main Authors: Zhao, Zhibin, Wang, Shibin, Wong, David, Wang, Wendong, Yan, Ruqiang, Chen, Xuefeng
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
Algorithms
Complexity
Computational complexity
Computational modeling
Convex optimization
Convexity
Decomposition
Dictionaries
Discrimination
enhanced fault diagnosis
Fault diagnosis
morphological component analysis
Morphology
Parameters
Signal resolution
Sparsity
sparsity-assisted signal decomposition
Time-domain analysis
Time-frequency analysis
ISSN:1083-4435, 1941-014X
Online Access:Get full text
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Summary:Sparsity-assisted signal decomposition (SASD) based on morphological component analysis (MCA) for bearing fault diagnosis has been studied in-depth. However, existing algorithms often use different combinations of representation dictionaries and priors, leading to difficult dictionary choice and high computational complexity. This article aims to develop a fast sparsity-assisted algorithm to decompose a vibration signal into discrete frequency and impulse components for bearing fault diagnosis. We introduce the morphological discrimination of discrete frequency and impulse components in time and frequency domains, respectively, for the first time. To use this morphological discrimination, we establish a fast SASD based on MCA with nonconvex enhancement. We further prove the necessary and sufficient condition to guarantee the convexity and use the majorization minimization algorithm to derive a fast solver. The proposed algorithm not only has low computational complexity, but also avoids choosing multiple dictionaries as well as underestimation of impulse features. Furthermore, an adaptive parameter selection algorithm to set parameters of our algorithm is designed for real applications. The effectiveness of fast SASD and its adaptive variant is verified by both simulation studies and bearing diagnosis cases. The source codes will be released at https://github.com/ZhaoZhibin/Fast_SASD .
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ISSN:1083-4435
1941-014X
DOI:10.1109/TMECH.2021.3103287