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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Wangpeng He & Peipei Zhang & Xuan Liu & Binqiang Chen & Baolong Guo, 2022. "Group-Sparse Feature Extraction via Ensemble Generalized Minimax-Concave Penalty for Wind-Turbine-Fault Diagnosis," Sustainability, MDPI, vol. 14(24), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16793-:d:1003529
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    References listed on IDEAS

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    1. Shuting Wan & Xiong Zhang & Longjiang Dou, 2018. "Compound Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by MCDK," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-12, March.
    2. Wei Fan & Gaigai Cai & Weiguo Huang & Li Shang & Zhongkui Zhu, 2014. "Sparse Representation of Transients Based on Wavelet Basis and Majorization-Minimization Algorithm for Machinery Fault Diagnosis," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-11, June.
    3. Xiaobo Liu & Haifei Ma & Yibing Liu, 2022. "A Novel Transfer Learning Method Based on Conditional Variational Generative Adversarial Networks for Fault Diagnosis of Wind Turbine Gearboxes under Variable Working Conditions," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
    4. Meng-Hui Wang & Shiue-Der Lu & Cheng-Che Hsieh & Chun-Chun Hung, 2022. "Fault Detection of Wind Turbine Blades Using Multi-Channel CNN," Sustainability, MDPI, vol. 14(3), pages 1-17, February.
    5. Cong Wang & Meng Gan & Chang’an Zhu, 2017. "Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1377-1391, August.
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