期刊
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
卷 69, 期 7, 页码 4863-4872出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2019.2955795
关键词
Convex functions; Fault diagnosis; Feature extraction; Signal resolution; Vibrations; Linear programming; Transient analysis; Bearing fault diagnosis; convex optimization; feature extraction; signal decomposition
资金
- National Natural Science Foundation of China [51405320, 51875376, 51705349, 51805342, 51605319]
- Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX19_1926]
Bearing fault diagnosis is critical for rotating machinery condition monitoring since it is a key component of rotating machines. One of the challenges for bearing fault diagnosis is to accurately realize fault feature extraction from original vibration signals. To tackle this problem, the novel group sparsity signal decomposition method is proposed in this article. For the sparsity within and across groups' property of the bearing vibration signals, the nonconvex group separable penalty is introduced to construct the objective function, leading to that the noise between the adjacent impulses can be eliminated and the impulses can be effectively extracted. Furthermore, since the penalty function is nonconvex, the convexity condition of the corresponding objective function to the global minimum is discussed. In addition, to improve the efficiency of parameter selection, this article presents an adaptive regularization parameter selection strategy. Simulation and experimental studies show that compared with the traditional method, the proposed method can better preserve the target components and reducing uncorrelated interference components for bearing fault diagnosis.
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