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Prediction of rolling bearing performance degradation based on sae and TCN-attention models

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Abstract

A single feature cannot show the operational state of a bearing during its entire life cycle. Therefore, a rolling bearing performance deterioration prediction method based on an SAE and the TCN-attention model is proposed. The SAE method is used to fuse the time-domain indicator and the frequency-domain indicator to construct the performance degradation characteristic indicator. The evaluation indices are used to comprehensively evaluate multiple performance degradation indices, and the fused feature indices together, to filter out the features that have a good overall performance. Attention is added to the TCN model, and the output state weight of the TCN model is calculated through a scoring function to increase the important information weight and the prediction accuracy. The appropriate network structure and parameter configuration are determined, and the rolling bearing performance degradation prediction model is established. A validation is performed using publicly available datasets from the University of Cincinnati and XJTU-SY. The results show that the method is more sensitive to the critical information part of the long time series than the other models. At the same time, the average absolute error and the root mean square error are minimized, the accuracy of the rolling bearing performance degradation prediction is high, and the model has a strong robustness and generalization abilities. Additionally, the model has practical engineering value for predicting the health status of equipment.

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Abbreviations

W 1 :

The weight matrix

b 1 :

The bias matrix

φ f(•):

Activation function of the input layer to the hidden layer

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Acknowledgments

The authors would like to thank Key Laboratory of Advanced Manufacturing Intelligent Technology of Ministry of Education for helpful discussions on topics related to this work. The authors are grateful to Dekang Hou for his help with the preparation of figure in this paper.

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Authors and Affiliations

  1. Key Laboratory of Advanced Manufacturing Intelligent Technology of Ministry of Education, Harbin University of Science and Technology, Harbin, Heilongjiang Province, 150080, China

    Yaping Wang

  2. School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, Heilongjiang Province, 150080, China

    Yaping Wang, Dekang Hou, Di Xu, Sheng Zhang & Chaonan Yang

Authors
  1. Yaping Wang
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  2. Dekang Hou
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  3. Di Xu
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  4. Sheng Zhang
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Corresponding author

Correspondence to Sheng Zhang.

Additional information

Yaping Wang was born in Liaoning province, China, in 1972. She received her M.S. degree in Mechanical and Electronic Engineering and Ph.D. degree in Mechanical Manufacturing and Automation from Harbin University of Science and Technology in 2006 and 2010, respectively. She has been a Professor and an M.S. Supervisor with Harbin University of Science and Technology. Her current research interests include signal processing, intelligent fault diagnosis and remaining useful life prediction.

Dekang Hou was born in Zhejiang province, China, in 1999. He received his B.S. degree in Automotive Service Engineering from Zhejiang University City College in 2021. He is currently pursuing the M.S. degree in Mechanical Design and Theory from Harbin University of Science and Technology. His current research interests include rolling bearing fault diagnosis and life prediction.

Di Xu was born in Heilongjiang province, China, in 1991. She received her B.S. degree in Mechanical Engineering and Automation from Harbin Far East Institute of Technology in 2014 and received her M.S. degrees in Mechanical Design and Theory from Harbin University of Science and Technology in 2017. She is currently pursuing the Ph.D. degree in Mechanical and Electronic Engineering from Harbin University of Science and Technology. Her current research interests include rotating machinery fault diagnosis and equipment health management.

Sheng Zhang was born in Jiangsu province, China, in 1997. He received his B.S. degree in Mechanical and Electronic Engineering from Jiangsu University in 2020. He is currently pursuing the M.S. degree in Mechanical Design and Theory from Harbin University of Science and Technology. His current research interests include rolling bearing fault diagnosis and life prediction.

Chaonan Yang was born in Henan province, China, in 1994. He received a Bachelor’s degree in Vehicle Engineering from the International Education College of Henan University of Science and Technology in 2019. He is currently pursuing the M.S. degree in Mechanical Design and Theory from Harbin University of Science and Technology. His current research interests include rolling bearing fault diagnosis and life prediction.

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Wang, Y., Hou, D., Xu, D. et al. Prediction of rolling bearing performance degradation based on sae and TCN-attention models. J Mech Sci Technol 37, 1567–1583 (2023). https://doi.org/10.1007/s12206-023-0301-4

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  • DOI: https://doi.org/10.1007/s12206-023-0301-4

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