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A health image for deep learning-based fault diagnosis of a permanent magnet synchronous motor under variable operating conditions: Instantaneous current residual map

Author

Listed:
  • Park, Chan Hee
  • Kim, Hyeongmin
  • Suh, Chaehyun
  • Chae, Minseok
  • Yoon, Heonjun
  • Youn, Byeng D.

Abstract

To take full advantage of a convolutional neural network (CNN) for deep learning-based fault diagnosis, many studies have examined the transformation of sensory signals into a two-dimensional (2D) input image. An important question to consider is: how can fault-related signatures in motor stator current signals be incorporated into the 2D input image to a CNN model for fault diagnosis of a permanent magnet synchronous motor (PMSM)? To answer the question, this study newly proposes a novel health image, namely instantaneous current residual map (ICRM). Inspired by the idea that the phase and amplitude modulations in motor stator current signals are related to faulty states of a PMSM, the overall procedure for constructing ICRM includes two key steps: (1) to calculate current residuals (CRs); and (2) to spread the scaled CR pairs into a 2D matrix. A type of faults can be figured out by analyzing a degree or shape of spreading of the CRs in ICRM. Moreover, ICRM is robust to variable operating conditions in practical settings because the scaled CRs that the effects of the operating conditions are reduced can highlight fault-induced irregularities. To demonstrate the effectiveness of ICRM, it was experimentally validated using a surface mounted PMSM, operated under variable-speed and different load torque conditions.

Suggested Citation

  • Park, Chan Hee & Kim, Hyeongmin & Suh, Chaehyun & Chae, Minseok & Yoon, Heonjun & Youn, Byeng D., 2022. "A health image for deep learning-based fault diagnosis of a permanent magnet synchronous motor under variable operating conditions: Instantaneous current residual map," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
  • Handle: RePEc:eee:reensy:v:226:y:2022:i:c:s0951832022003398
    DOI: 10.1016/j.ress.2022.108715
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    References listed on IDEAS

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    1. Li, Rui & Verhagen, Wim J.C. & Curran, Richard, 2020. "A systematic methodology for Prognostic and Health Management system architecture definition," Reliability Engineering and System Safety, Elsevier, vol. 193(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. Gao, Lu & Lu, Pan & Ren, Yihao, 2021. "A deep learning approach for imbalanced crash data in predicting highway-rail grade crossings accidents," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    4. Li, Xiang & Zhang, Wei & Ding, Qian, 2019. "Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 208-218.
    5. 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).
    6. 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).
    7. 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.
    8. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
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    3. Xia, Pengcheng & Huang, Yixiang & Tao, Zhiyu & Liu, Chengliang & Liu, Jie, 2023. "A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
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    7. Gao, Dawei & Huang, Kai & Zhu, Yongsheng & Zhu, Linbo & Yan, Ke & Ren, Zhijun & Guedes Soares, C., 2024. "Semi-supervised small sample fault diagnosis under a wide range of speed variation conditions based on uncertainty analysis," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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