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A Migration Learning Method Based on Adaptive Batch Normalization Improved Rotating Machinery Fault Diagnosis

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  • Xueyi Li

    (College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
    Key Laboratory of Vibration and Control of Aero-Propulsion System, Ministry of Education, Northeastern University, Shenyang 110819, China)

  • Tianyu Yu

    (College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China)

  • Daiyou Li

    (College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China)

  • Xiangkai Wang

    (College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China)

  • Cheng Shi

    (School of Vehicle and Energy, Yanshan University, Qinhuangdao 066004, China)

  • Zhijie Xie

    (College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China)

  • Xiangwei Kong

    (Key Laboratory of Vibration and Control of Aero-Propulsion System, Ministry of Education, Northeastern University, Shenyang 110819, China
    School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China)

Abstract

Sustainable development has become increasingly important as one of the key research directions for the future. In the field of rotating machinery, stable operation and sustainable performance are critical, focusing on the fault diagnosis of component bearings. However, traditional normalization methods are ineffective in target domain data due to the difference in data distribution between the source and target domains. To overcome this issue, this paper proposes a bearing fault diagnosis method based on the adaptive batch normalization algorithm, which aims to enhance the generalization ability of the model in different data distributions and environments. The adaptive batch normalization algorithm improves the adaptability and generalization ability to better respond to changes in data distribution and the real-time requirements of practical applications. This algorithm replaces the statistical values in a BN with domain adaptive mean and variance statistics to minimize feature differences between two different domains. Experimental results show that the proposed method outperforms other methods in terms of performance and generalization ability, effectively solving the problems of data distribution changes and real-time requirements in bearing fault diagnosis. The research results indicate that the adaptive batch normalization algorithm is a feasible method to improve the accuracy and reliability of bearing fault diagnosis.

Suggested Citation

  • Xueyi Li & Tianyu Yu & Daiyou Li & Xiangkai Wang & Cheng Shi & Zhijie Xie & Xiangwei Kong, 2023. "A Migration Learning Method Based on Adaptive Batch Normalization Improved Rotating Machinery Fault Diagnosis," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8034-:d:1147367
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    References listed on IDEAS

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