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

Author

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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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    References listed on IDEAS

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    1. Xu, Yadong & Yan, Xiaoan & Sun, Beibei & Liu, Zheng, 2022. "Global contextual residual convolutional neural networks for motor fault diagnosis under variable-speed conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. 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.
    3. Vrignat, Pascal & Kratz, Frédéric & Avila, Manuel, 2022. "Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    4. Tang, Shengnan & Zhu, Yong & Yuan, Shouqi, 2022. "Intelligent fault identification of hydraulic pump using deep adaptive normalized CNN and synchrosqueezed wavelet transform," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    5. Kumar, Anil & Parkash, Chander & Vashishtha, Govind & Tang, Hesheng & Kundu, Pradeep & Xiang, Jiawei, 2022. "State-space modeling and novel entropy-based health indicator for dynamic degradation monitoring of rolling element bearing," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    6. Xiao, Sinan & Lu, Zhenzhou & Wang, Pan, 2018. "Multivariate global sensitivity analysis for dynamic models based on wavelet analysis," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 20-30.
    7. Guo, Jianchun & Si, Zetian & Liu, Yi & Li, Jiahao & Li, Yanting & Xiang, Jiawei, 2022. "Dynamic time warping using graph similarity guided symplectic geometry mode decomposition to detect bearing faults," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    8. Peters, Benjamin & Yildirim, Murat & Gebraeel, Nagi & Paynabar, Kamran, 2020. "Severity-based diagnosis for vehicular electric systems with multiple, interacting fault modes," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    9. Saraygord Afshari, Sajad & Enayatollahi, Fatemeh & Xu, Xiangyang & Liang, Xihui, 2022. "Machine learning-based methods in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
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    Cited by:

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    2. Zhao, Dezun & Huang, Xiaofan & Wang, Tianyang & Cui, Lingli, 2025. "Generalized reassigning transform: Algorithm and applications," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
    3. 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).
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    5. Zhang, Zhongwei & Jiao, Zonghao & Li, Youjia & Shao, Mingyu & Dai, Xiangjun, 2024. "Intelligent fault diagnosis of bearings driven by double-level data fusion based on multichannel sample fusion and feature fusion under time-varying speed conditions," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    6. 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).
    7. Jiang, Yixin & Zhou, Jun & Wu, Xing & Liu, Tao & Liu, Xiaoqin, 2025. "Vision-based bearing fault diagnosis under non-stationary conditions using optimized short-time concentrated transform method," Reliability Engineering and System Safety, Elsevier, vol. 262(C).
    8. Zhang, Qing & Li, Shaochen & Chin-Hon, Tan & Liu, Xiaofei & Shen, Jingyuan & Shi, Tielin & Xuan, Jianping, 2025. "Fault Impulse Inference and Cyclostationary Approximation: A feature-interpretable intelligent fault detection method for few-shot unsupervised domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    9. 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).

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