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A novel data augmentation approach to fault diagnosis with class-imbalance problem

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  • Tian, Jilun
  • Jiang, Yuchen
  • Zhang, Jiusi
  • Luo, Hao
  • Yin, Shen

Abstract

Data-driven fault diagnosis techniques are frequently applied to ensure the reliability and safety of industrial systems. However, as a common challenge, the class-imbalance problem reduces the performance of data-driven methods due to the lack of data information. We propose a weighted modified conditional variational auto-encoder (WM-CVAE) as a novel data augmentation technique to tackle the issue. The modified structure can alleviate the existing Kullback–Leibler (KL) divergence vanishing by an adaptive loss. Meanwhile, kernel mean matching (KMM) is proposed on weight computation to reduce the negative effect of dissimilar generated samples. Constructing the WM-CVAE data augmentation framework can effectively improve the data quality and learning capability in class-imbalance fault diagnosis. To validate the proposed WM-CVAE model, three real-world industrial datasets are used as study objects, and the random forest is used as the base learner in the fault classification tasks. The diagnostic results demonstrate that the proposed WM-CVAE data augmentation framework can improve learning results in class-imbalance fault diagnosis.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:243:y:2024:i:c:s0951832023007469
    DOI: 10.1016/j.ress.2023.109832
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    References listed on IDEAS

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    1. Shi, Peiming & Wu, Shuping & Xu, Xuefang & Zhang, Bofei & Liang, Pengfei & Qiao, Zijian, 2023. "TSN: A novel intelligent fault diagnosis method for bearing with small samples under variable working conditions," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    2. 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).
    3. Melani, Arthur Henrique de Andrade & Michalski, Miguel Angelo de Carvalho & da Silva, Renan Favarão & de Souza, Gilberto Francisco Martha, 2021. "A framework to automate fault detection and diagnosis based on moving window principal component analysis and Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. 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).
    5. Leoni, Leonardo & De Carlo, Filippo & Abaei, Mohammad Mahdi & BahooToroody, Ahmad & Tucci, Mario, 2023. "Failure diagnosis of a compressor subjected to surge events: A data-driven framework," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    6. Pedro Santos & Jesús Maudes & Andres Bustillo, 2018. "Identifying maximum imbalance in datasets for fault diagnosis of gearboxes," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 333-351, February.
    7. 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).
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    Cited by:

    1. Zhang, Jiusi & Tian, Jilun & Yan, Pengfei & Wu, Shimeng & Luo, Hao & Yin, Shen, 2024. "Multi-hop graph pooling adversarial network for cross-domain remaining useful life prediction: A distributed federated learning perspective," Reliability Engineering and System Safety, Elsevier, vol. 244(C).

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