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A case-learning-based paradigm for quantitative recommendation of fault diagnosis algorithms: A case study of gearbox

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  • Zou, Xinyu
  • Tao, Laifa
  • Sun, Lulu
  • Wang, Chao
  • Ma, Jian
  • Lu, Chen

Abstract

Prognostics and Health Management (PHM) is a core technology for condition-based maintenance. The diversity of PHM algorithms and the complexity of design factors make it challenging to choose an appropriate algorithm for a specific application. Consequently, automatic PHM algorithm recommendation (PHM-AR) is of great significance for PHM developers to implement rapid design of PHM systems. However, the two existing paradigms for PHM-AR rely heavily on expert experience and failure data respectively. In this paper, we propose a new third paradigm, called the case-learning-based paradigm, for quantitative recommendation of fault diagnosis algorithms, focusing on diagnosis task attribute analysis, multi-dimensional feature representation, and recommendation model construction. Specifically, to provide a quantitative basis for the recommendation of diagnosis algorithms, we define a three-level attribute set of diagnosis tasks and propose a quantitative representation method to make data structured. Then, a recommendation model based on the Classification and Regression Tree (CART) is proposed. Finally, taking gearbox as an example, we construct a diagnosis case set and verify the effectiveness of the proposed framework. Experimental results show that the average recommendation accuracy of our proposed method reaches 70.40%. These results also elucidate that the proposed method can learn the applicable rules of various diagnosis algorithms.

Suggested Citation

  • Zou, Xinyu & Tao, Laifa & Sun, Lulu & Wang, Chao & Ma, Jian & Lu, Chen, 2023. "A case-learning-based paradigm for quantitative recommendation of fault diagnosis algorithms: A case study of gearbox," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023002867
    DOI: 10.1016/j.ress.2023.109372
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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. Compare, Michele & Antonello, Federico & Pinciroli, Luca & Zio, Enrico, 2022. "A general model for life-cycle cost analysis of Condition-Based Maintenance enabled by PHM capabilities," Reliability Engineering and System Safety, Elsevier, vol. 224(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. Xu, Yadong & Yan, Xiaoan & Feng, Ke & Sheng, Xin & Sun, Beibei & Liu, Zheng, 2022. "Attention-based multiscale denoising residual convolutional neural networks for fault diagnosis of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. 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).
    6. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    7. Hu, Yang & Miao, Xuewen & Si, Yong & Pan, Ershun & Zio, Enrico, 2022. "Prognostics and health management: A review from the perspectives of design, development and decision," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    8. 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).
    9. Soleimani, Morteza & Campean, Felician & Neagu, Daniel, 2021. "Integration of Hidden Markov Modelling and Bayesian Network for fault detection and prediction of complex engineered systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    10. Wei Zhang & Xiaohui Yang & Yeheng Deng & Anyi Li, 2020. "An Inspired Machine-Learning Algorithm with a Hybrid Whale Optimization for Power Transformer PHM," Energies, MDPI, vol. 13(12), pages 1-17, June.
    11. 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.
    12. Mancuso, A. & Compare, M. & Salo, A. & Zio, E., 2021. "Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    Full references (including those not matched with items on IDEAS)

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