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A novel normalized recurrent neural network for fault diagnosis with noisy labels

Author

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  • Xiaoyin Nie

    (Taiyuan University of Science and Technology
    Taiyuan Institute of Technology)

  • Gang Xie

    (Taiyuan University of Science and Technology)

Abstract

The early fault diagnosis is a kind of important technology to ensure the normal and reliable operation of wind turbines. However, due to the potential presence of noisy labels in health condition dataset and the weakly explanation of the deep neural network decisions, the performance of fault diagnosis is severely limited. In this paper, a framework called normalized recurrent neural network (NRNN) is proposed for noisy label fault diagnosis, in which the normalized long short-term memory is used to improve the training process and the forward crossentropy loss is introduced to handle the negative effect of noisy labels. The effectiveness and superiority of the proposed framework are verified by four datasets with different noisy label proportions. Meanwhile, the layer-wise relevance propagation algorithm is applied to explore the decision of framework and by visualizing the relevances of input samples to framework decisions, the NRNN does not treat samples equally and prefers signal peaks for classification decisions.

Suggested Citation

  • Xiaoyin Nie & Gang Xie, 2021. "A novel normalized recurrent neural network for fault diagnosis with noisy labels," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1271-1288, June.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:5:d:10.1007_s10845-020-01608-8
    DOI: 10.1007/s10845-020-01608-8
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    References listed on IDEAS

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    1. Cong Wang & Meng Gan & Chang’an Zhu, 2018. "Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 937-951, April.
    2. Deepam Goyal & Anurag Choudhary & B. S. Pabla & S. S. Dhami, 2020. "Support vector machines based non-contact fault diagnosis system for bearings," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1275-1289, June.
    3. Chen, Jinglong & Jing, Hongjie & Chang, Yuanhong & Liu, Qian, 2019. "Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 372-382.
    4. Jialin Li & Xueyi Li & David He & Yongzhi Qu, 2020. "Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1899-1916, December.
    5. Cong Wang & Meng Gan & Chang’an Zhu, 2019. "A supervised sparsity-based wavelet feature for bearing fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 229-239, January.
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    Cited by:

    1. Ma, Yulin & Li, Lei & Yang, Jun, 2022. "Convolutional kernel aggregated domain adaptation for intelligent fault diagnosis with label noise," Reliability Engineering and System Safety, Elsevier, vol. 227(C).

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