TY - JOUR
T1 - Sparsity-enhanced signal decomposition via generalized minimax-concave penalty for gearbox fault diagnosis
AU - Cai, Gaigai
AU - Selesnick, Ivan W.
AU - Wang, Shibin
AU - Dai, Weiwei
AU - Zhu, Zhongkui
N1 - Funding Information:
This work was supported by the National Nature Science Foundation of China (No. 51405321, 51605366, and 51375322), the China Postdoctoral Science Foundation (No. 2016M590937, 2017T100740), and the Fundamental Research Funds for the Central Universities. Thanks also go to the anonymous reviewers for their constructive suggestions.
Funding Information:
This work was supported by the National Nature Science Foundation of China (No. 51405321 , 51605366 , and 51375322 ), the China Postdoctoral Science Foundation (No. 2016M590937 , 2017T100740 ), and the Fundamental Research Funds for the Central Universities . Thanks also go to the anonymous reviewers for their constructive suggestions.
Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/10/13
Y1 - 2018/10/13
N2 - Vibration signals arising from faulty gearboxes are often a mixture of the meshing component and the periodic transient component, and simultaneously contaminated by noise. Sparsity-assisted signal decomposition is an effective technique to decompose a signal into morphologically distinct components based on sparse representation and optimization. In this paper, we propose a sparsity-enhanced signal decomposition method which uses the generalized minimax-concave (GMC) penalty as a nonconvex regularizer to enhance sparsity in the sparse approximation compared to classical sparsity-assisted signal decomposition methods, and thus to improve the decomposition accuracy for gearbox fault diagnosis. Even though the GMC penalty itself is nonconvex, it maintains the convexity of the GMC regularized cost function to be minimized. Hence, similar to the classical L1-norm regularization methods, the global optimal solution can be guaranteed via convex optimization. Moreover, we present and validate a straight-forward way to choose transforms and set parameters for the proposed method. Through simulation studies, it is demonstrated that the proposed sparsity-enhanced signal decomposition method can effectively decompose the simulated faulty gearbox signal into the meshing component and the periodic transient component. Comparisons with the classical L1-norm regularized signal decomposition method and spectral kurtosis show that the proposed method can accurately preserve the amplitude of the periodic transient component and provide a more accurate estimation result. Experiment and engineering case studies further verify that the proposed method can accurately estimate the periodic transient component from vibration signals, which demonstrate that the proposed method is a promising tool for gearbox fault diagnosis.
AB - Vibration signals arising from faulty gearboxes are often a mixture of the meshing component and the periodic transient component, and simultaneously contaminated by noise. Sparsity-assisted signal decomposition is an effective technique to decompose a signal into morphologically distinct components based on sparse representation and optimization. In this paper, we propose a sparsity-enhanced signal decomposition method which uses the generalized minimax-concave (GMC) penalty as a nonconvex regularizer to enhance sparsity in the sparse approximation compared to classical sparsity-assisted signal decomposition methods, and thus to improve the decomposition accuracy for gearbox fault diagnosis. Even though the GMC penalty itself is nonconvex, it maintains the convexity of the GMC regularized cost function to be minimized. Hence, similar to the classical L1-norm regularization methods, the global optimal solution can be guaranteed via convex optimization. Moreover, we present and validate a straight-forward way to choose transforms and set parameters for the proposed method. Through simulation studies, it is demonstrated that the proposed sparsity-enhanced signal decomposition method can effectively decompose the simulated faulty gearbox signal into the meshing component and the periodic transient component. Comparisons with the classical L1-norm regularized signal decomposition method and spectral kurtosis show that the proposed method can accurately preserve the amplitude of the periodic transient component and provide a more accurate estimation result. Experiment and engineering case studies further verify that the proposed method can accurately estimate the periodic transient component from vibration signals, which demonstrate that the proposed method is a promising tool for gearbox fault diagnosis.
KW - Convex optimization
KW - Gearbox fault diagnosis
KW - Generalized minimax-concave penalty
KW - Signal decomposition
KW - Sparse representation
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U2 - 10.1016/j.jsv.2018.06.037
DO - 10.1016/j.jsv.2018.06.037
M3 - Article
AN - SCOPUS:85048946960
SN - 0022-460X
VL - 432
SP - 213
EP - 234
JO - Journal of Sound and Vibration
JF - Journal of Sound and Vibration
ER -