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Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit

Author

Listed:
  • Cong Wang

    (University of Science and Technology of China (USTC))

  • Meng Gan

    (University of Science and Technology of China (USTC))

  • Chang’an Zhu

    (University of Science and Technology of China (USTC))

Abstract

This paper explores sparse time-frequency distribution (TFD) using overcomplete discrete wavelet transform (DWT) and sparse representation techniques. This distribution is discovered for characterizing the periodic transient information embedded in rolling element bearings and extracting effective features that can discriminate different fault conditions. Based on the sparse TFD, a new sparse wavelet energy (SWE) feature is obtained by three main steps: first, an overcomplete discrete DWT is employed to decompose the fault signal and construct a redundant dictionary; second, the redundant dictionary is optimized by basis pursuit to obtain the sparsest TFD; finally, SWE is calculated from the new TFD to produce a feature vector for each signal. SWE features that combine the merits of overcomplete DWT and sparse representation techniques can precisely reveal fault-induced information, thereby exhibiting valuable properties for automatic fault identification by intelligent classifiers. The effectiveness and advantages of the proposed features are confirmed by simulation and the practical fault pattern recognition of rolling bearings.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:6:d:10.1007_s10845-015-1056-2
    DOI: 10.1007/s10845-015-1056-2
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    Cited by:

    1. Jialin Li & Xueyi Li & David He & Yongzhi Qu, 2020. "Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1899-1916, December.
    2. Gang Wang & Feng Zhang & Bayi Cheng & Fang Fang, 2021. "DAMER: a novel diagnosis aggregation method with evidential reasoning rule for bearing fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 1-20, January.
    3. Xiang Li & Wei Zhang & Qian Ding & Jian-Qiao Sun, 2020. "Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 433-452, February.
    4. Ke Zhao & Hongkai Jiang & Zhenghong Wu & Tengfei Lu, 2022. "A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 151-165, January.
    5. Jinhai Chen & Wenyuan Zhang & Heng Wang, 2021. "Intelligent bearing structure and temperature field analysis based on finite element simulation for sustainable and green manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 745-756, March.
    6. Rubén Medina & Jean Carlo Macancela & Pablo Lucero & Diego Cabrera & René-Vinicio Sánchez & Mariela Cerrada, 2022. "Gear and bearing fault classification under different load and speed by using Poincaré plot features and SVM," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1031-1055, April.
    7. 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.

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