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Dynamic time warping using graph similarity guided symplectic geometry mode decomposition to detect bearing faults

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  • Guo, Jianchun
  • Si, Zetian
  • Liu, Yi
  • Li, Jiahao
  • Li, Yanting
  • Xiang, Jiawei

Abstract

A bearing's health state is closely linked to the reliable operation of rotating machinery. In this context, dynamic time warping (DTW) is an excellent fault classifier due to its outstanding distance measurement ability. However, DTW alone cannot obtain acceptable results when it is employed to handle signals with a certain degree of noise. An enhancement of DTW based on graph similarity guided symplectic geometry mode decomposition (GS-SGMD) is presented in this paper to improve the performance of traditional DTW. To reduce the interference of random noise in raw signals, the signal is decomposed into multiple components by SGMD. Then, graph similarity is introduced to select the effective component as a test sample to be detected. In addition, the templates of known fault states of bearings are also obtained by GS-SGMD. Finally, DTW is employed to recognize the fault type of the test sample. Experimental results show that the presented method can effectively detect bearing faults with higher precisions in comparison to DTW and SGMD.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:224:y:2022:i:c:s0951832022001879
    DOI: 10.1016/j.ress.2022.108533
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    References listed on IDEAS

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    1. 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).
    2. Wang, Xu & Shen, Changqing & Xia, Min & Wang, Dong & Zhu, Jun & Zhu, Zhongkui, 2020. "Multi-scale deep intra-class transfer learning for bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    3. Xia, Min & Shao, Haidong & Williams, Darren & Lu, Siliang & Shu, Lei & de Silva, Clarence W., 2021. "Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. 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).
    5. Manjurul Islam, M.M. & Kim, Jong-Myon, 2019. "Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 55-66.
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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. Xiang, Jiawei & Guo, Jianchun & Li, Xiaoqi, 2024. "A two-stage Duffing equation-based oscillator and stochastic resonance for mechanical fault diagnosis," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    3. Ma, Chenyang & Wang, Xianzhi & Li, Yongbo & Cai, Zhiqiang, 2024. "Broad zero-shot diagnosis for rotating machinery with untrained compound faults," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    4. Wang, Hui & Zheng, Junkang & Xiang, Jiawei, 2023. "Online bearing fault diagnosis using numerical simulation models and machine learning classifications," Reliability Engineering and System Safety, Elsevier, vol. 234(C).

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