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Bayes-DCGRU with bayesian optimization for rolling bearing fault diagnosis

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Abstract

Deep learning has been diffusely used in bearing fault diagnosis. In view of the increasing complexity of the model and the exponential growth of the hyperparameters, the adjustment becomes increasingly difficult. In this paper, a Bayesian optimization-Deep convolution gate recurring unit (Bayes-DCGRU) based on Bayesian optimization is proposed. It adopts Bayesian optimization algorithm to automatically adjust the hyperparameters of the model, and convolutional neural network (CNN) adaptively extracts the spatial characteristics of bearing signals. Combined with the Gate recurring unit (GRU) to learn the time series characteristics of signals, and then achieve high precision bearing fault identification. This method overcomes the shortcomings of the traditional hyperparameter adjustment method based on experience and feeling. It provides a solution for the hyperparameter adjustment of bearing fault diagnosis model under multi-hyperparameter coupling. The experimental results show that the model obtained by this method converges quickly and the fault identification accuracy is higher.

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Acknowledgements

The authors would like to gratefully acknowledge the financial support of the Fundamental Research Funds for the Central Universities (N180304020).

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Northeastern University, Shenyang, 110819, China

    Ma Jiaocheng & Shang Jinan

  2. Key Laboratory of Lifting Equipment’s Safety Technology for State Market Regulation, Shenyang, 110819, China

    Ma Jiaocheng & Zhao Xin

  3. North Automatic Control Technology Institute, Taiyuan, 030000, China

    Shang Jinan

  4. Liaoning Provincial Institute of Safety Science, Shenyang, 110004, China

    Zhao Xin

  5. Shenyang Special Equipment Testing Research Institute, Shenyang, 110035, China

    Zhong Peng

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  1. Ma Jiaocheng
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  2. Shang Jinan
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  4. Zhong Peng
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Correspondence to Ma Jiaocheng.

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Jiaocheng, M., Jinan, S., Xin, Z. et al. Bayes-DCGRU with bayesian optimization for rolling bearing fault diagnosis. Appl Intell 52, 11172–11183 (2022). https://doi.org/10.1007/s10489-021-02924-z

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