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Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis

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  • Wu, Jingyao
  • Zhao, Zhibin
  • Sun, Chuang
  • Yan, Ruqiang
  • Chen, Xuefeng

Abstract

Considering the difficulty of data acquisition in industry, especially for failure data of large-scale equipment, classification with these class-imbalanced datasets can lead to the problems of minority categories overfitting and majority categories domination. A model-agnostic framework towards class-imbalanced fault diagnosis requirement is proposed to systematically alleviate these problems. Four sub-modules, including Time-series Data Augmentation, Data-Rebalanced sampler, Balanced Margin Loss, and classifier with Dynamic Decision Boundary Balancing are performed to improve recognition accuracy of minority categories without performance degradation on majority categories. Meanwhile, the framework is compatible with general neural networks and provides flexible model candidates to meet the need of feature extraction for different data types. Three case studies on public datasets demonstrate that proposed framework outperformed various state-of-the-art methods.

Suggested Citation

  • Wu, Jingyao & Zhao, Zhibin & Sun, Chuang & Yan, Ruqiang & Chen, Xuefeng, 2021. "Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021004506
    DOI: 10.1016/j.ress.2021.107934
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Li, Sai & Peng, Yanfeng & Shen, Yiping & Zhao, Sibo & Shao, Haidong & Bin, Guangfu & Guo, Yong & Yang, Xingkai & Fan, Chao, 2024. "Rolling Bearing Fault Diagnosis Under Data Imbalance and Variable Speed Based on Adaptive Clustering Weighted Oversampling," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    2. Zhao, Chao & Shen, Weiming, 2022. "Adaptive open set domain generalization network: Learning to diagnose unknown faults under unknown working conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    3. Tian, Jilun & Jiang, Yuchen & Zhang, Jiusi & Luo, Hao & Yin, Shen, 2024. "A novel data augmentation approach to fault diagnosis with class-imbalance problem," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    4. Guo, Junchao & He, Qingbo & Zhen, Dong & Gu, Fengshou & Ball, Andrew D., 2023. "Multi-sensor data fusion for rotating machinery fault detection using improved cyclic spectral covariance matrix and motor current signal analysis," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    5. Jin, Kyungho & Kim, Hyeonmin & Ryu, Seunghyoung & Kim, Seunggeun & Park, Jinkyun, 2022. "An approach to constructing effective training data for a classification model to evaluate the reliability of a passive safety system," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Yin, Zhenqin & Zhuo, Yue & Ge, Zhiqiang, 2023. "Transfer adversarial attacks across industrial intelligent systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    7. Zhao, Chao & Shen, Weiming, 2022. "Dual adversarial network for cross-domain open set fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    8. Ding, Yifei & Jia, Minping & Zhuang, Jichao & Cao, Yudong & Zhao, Xiaoli & Lee, Chi-Guhn, 2023. "Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

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