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Hierarchical discriminating sparse coding for weak fault feature extraction of rolling bearings

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  • Jiao, Jinyang
  • Zhao, Ming
  • Lin, Jing
  • Liang, Kaixuan

Abstract

The effective extraction of weak fault features is crucial in condition monitoring and fault diagnosis of rolling bearings. Sparse coding, as a promising tool for signal denoising and feature extraction, has attracted a lot of attention in recent years. However, many challenges still exist when sparse coding is applied to the bearings detection under harsh working conditions. Specifically, the predefined dictionary-based sparse coding (PDSC) usually needs prior knowledge about the target signal, while the learning dictionary-based sparse coding (LDSC) is susceptible to interfering components produced by other rotating parts, thus bringing difficulties for early fault identification. To overcome these disadvantages, a hierarchical discriminating sparse coding (HDSC) method is presented in this paper, which could process the raw signals directly and utilize hierarchical concept to isolate interferences. In addition, a novel index termed envelope harmonic-to-noise ratio (EHNR) is introduced to give the instruction on reasonably choosing the parameters in the process of HDSC. The advantages of HDSC over traditional approaches are validated on the simulated signals and real vibration data from locomotive bearing. The results demonstrate that the proposed method can successfully extract the weak fault feature even in the presence of strong noise and ambient interferences.

Suggested Citation

  • Jiao, Jinyang & Zhao, Ming & Lin, Jing & Liang, Kaixuan, 2019. "Hierarchical discriminating sparse coding for weak fault feature extraction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 41-54.
  • Handle: RePEc:eee:reensy:v:184:y:2019:i:c:p:41-54
    DOI: 10.1016/j.ress.2018.02.010
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    Citations

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    Cited by:

    1. Liu, Yi & Xiang, Hang & Jiang, Zhansi & Xiang, Jiawei, 2023. "Second-order transient-extracting S transform for fault feature extraction in rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    2. Zhou, Taotao & Han, Te & Droguett, Enrique Lopez, 2022. "Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    3. Yang, Zhe & Baraldi, Piero & Zio, Enrico, 2022. "A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networks," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    4. He, Jiahui & Cheng, Zhijun & Guo, Bo, 2024. "Anomaly detection in telemetry data using a jointly optimal one-class support vector machine with dictionary learning," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. Guan, Yang & Meng, Zong & Sun, Dengyun & Liu, Jingbo & Fan, Fengjie, 2021. "2MNet: Multi-sensor and multi-scale model toward accurate fault diagnosis of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Li, Xin & Zhong, Xiang & Shao, Haidong & Han, Te & Shen, Changqing, 2021. "Multi-sensor gearbox fault diagnosis by using feature-fusion covariance matrix and multi-Riemannian kernel ridge regression," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. Lee, Jinwook & Kim, Myungyon & Ko, Jin Uk & Jung, Joon Ha & Sun, Kyung Ho & Youn, Byeng D., 2022. "Asymmetric inter-intra domain alignments (AIIDA) method for intelligent fault diagnosis of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    8. Bai, Ruxue & Meng, Zong & Xu, Quansheng & Fan, Fengjie, 2023. "Fractional Fourier and time domain recurrence plot fusion combining convolutional neural network for bearing fault diagnosis under variable working conditions," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    9. Hongchao Wang & Wenliao Du, 2020. "A new K-means singular value decomposition method based on self-adaptive matching pursuit and its application in fault diagnosis of rolling bearing weak fault," International Journal of Distributed Sensor Networks, , vol. 16(5), pages 15501477209, May.

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