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Second-order transient-extracting S transform for fault feature extraction in rolling bearings

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  • Liu, Yi
  • Xiang, Hang
  • Jiang, Zhansi
  • Xiang, Jiawei

Abstract

Intelligent fault diagnosis methods can obtain promising results in ensuring the safety and reliability of key parts of rotating machinery. However, the problems are the insufficient amount of data during equipment acceptance period and the assumption that the collected data are high quality which directly affects the reliability of promising results. To solve the above problems, based on the characteristics of fault features, a time-frequency-based method is introduced to analyze the impulse components. Nevertheless, the performance of the time-frequency method is deeply relies on the selection of the window length. To avoid the influence of uncertain parameters, an accurate time-frequency analysis method named the second-order transient-extracting S transform based on the S-transform is proposed in this paper. The proposed method not only rectifies the group delay bias but also produces a highly concentrated time-frequency representation even in noise-surrounded and irrelevant components. The effectiveness of the proposed method for monitoring the health of key parts health is verified through simulated and experimental investigations. The accuracy of the proposed method in feature detection is higher than that of other methods.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005701
    DOI: 10.1016/j.ress.2022.108955
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    Cited by:

    1. Zhou, Haoxuan & Wang, Bingsen & Zio, Enrico & Wen, Guangrui & Liu, Zimin & Su, Yu & Chen, Xuefeng, 2023. "Hybrid system response model for condition monitoring of bearings under time-varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    2. Chaleshtori, Amir Eshaghi & Aghaie, Abdollah, 2024. "A novel bearing fault diagnosis approach using the Gaussian mixture model and the weighted principal component analysis," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    3. Sahebkar Farkhani, Jalal & Çelik, Özgür & Ma, Kaiqi & Bak, Claus Leth & Chen, Zhe, 2024. "A comprehensive review of potential protection methods for VSC multi-terminal HVDC systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).

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