Group-Sparse Feature Extraction via Ensemble Generalized Minimax-Concave Penalty for Wind-Turbine-Fault Diagnosis

Wangpeng He, Peipei Zhang, Xuan Liu, Binqiang Chen () and Baolong Guo
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Wangpeng He: School of Aerospace Science and Technology, Xidian University, Xi’an 710071, China
Peipei Zhang: School of Aerospace Science and Technology, Xidian University, Xi’an 710071, China
Xuan Liu: School of Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China
Binqiang Chen: School of Aerospace Engineering, Xiamen University, Xiamen 361005, China
Baolong Guo: School of Aerospace Science and Technology, Xidian University, Xi’an 710071, China

Sustainability, 2022, vol. 14, issue 24, 1-15

Abstract: Extracting weak fault features from noisy measured signals is critical for the diagnosis of wind turbine faults. In this paper, a novel group-sparse feature extraction method via an ensemble generalized minimax-concave (GMC) penalty is proposed for machinery health monitoring. Specifically, the proposed method tackles the problem of formulating large useful magnitude values as isolated features in the original GMC-based sparse feature extraction method. To accurately estimate group-sparse fault features, the proposed method formulates an effective unconstrained optimization problem wherein the group-sparse structure is incorporated into non-convex regularization. Moreover, the convex condition is proved to maintain the convexity of the whole formulated cost function. In addition, the setting criteria of the regularization parameter are investigated. A simulated signal is presented to verify the performance of the proposed method for group-sparse feature extraction. Finally, the effectiveness of the proposed group-sparse feature extraction method is further validated by experimental fault diagnosis cases.

Keywords: group-sparse signal; wind-turbine-fault diagnosis; convex optimization; feature extraction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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