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Application of recurrent neural network to mechanical fault diagnosis: a review

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Abstract

With the development of intelligent manufacturing and automation, the precision and complexity of mechanical equipment are increasing, which leads to a higher requirement for fault diagnosis. Fault diagnosis has gradually transformed from traditional diagnosis algorithm to deep feature mining and expression of highly nonlinear, complex and multidimensional systems. At present, the mechanical fault signals of various equipment are mostly time series. In addition, recurrent neural network (RNN) has strong nonlinear feature learning and processing ability of time sequence information, which has achieved promising results in mechanical fault diagnosis and big data processing. Therefore, this study reviews state-of-the-art RNN method in mechanical fault diagnosis and introduces applications from two aspects: RNN and the combined neural networks which include RNN. Then, this paper discusses the challenges and future development of RNN based fault diagnosis.

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Acknowledgments

This research was funded by National Natural Science Foundation of China (51975394) and the Open Fund of Key Laboratory for Metallurgical Equipment and Control Technology of Ministry of Education in Wuhan University of Science and Technology (MECOF2020B05).

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Authors and Affiliations

  1. School of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China

    Junjun Zhu, Quansheng Jiang, Yehu Shen, Chenhui Qian & Qixin Zhu

  2. College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Fengyu Xu

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  1. Junjun Zhu
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  2. Quansheng Jiang
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  3. Yehu Shen
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  4. Chenhui Qian
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  5. Fengyu Xu
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Corresponding author

Correspondence to Quansheng Jiang.

Additional information

Junjun Zhu received his B.S. from Suzhou University of Science and Technology, China, in 2019. He is an M.D. candidate of Suzhou University of Science and Technology. His research interests include deep learning, mechanical fault diagnosis and remaining useful life prediction of rotating machinery.

Quansheng Jiang received his Ph.D. in Mechanical Engineering from Southeast University, China, in 2009. He is an Associate Professor of Mechanical Engineering, Suzhou University of Science and Technology, China. His research interests include signal processing and fault detection for mechanical systems.

Chenhui Qian received the B.S. from Suzhou University of Science and Technology, China, in 2019. Now he is an M.D. candidate in Mechanical Engineering, Suzhou University of Science and Technology, China. His current research interests include rotating machinery fault diagnosis, machine learning and deep migration learning.

Yehu Shen received his Ph.D. in Communication and Information Systems from Zhejiang University, China, in 2009. He is an Associate Professor of Mechanical Engineering, Suzhou University of Science and Technology, China. His research interests include pattern recognition and image processing technology.

Fengyu Xu received his Ph.D. in Mechanical Engineering from Southeast University, China, in 2009. He is a Professor at College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China. His research interests include robots and automation, mechatronics technology, intelligent manufacturing equipment and control technology.

Qixin Zhu received his Ph.D. in Control Theory and Control Engineering from Nanjing University of Aeronautics and Astronautics in 2003. He is a Professor of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou, China. His research interests include intelligent control, robots, and the applications of control theory in engineering.

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Zhu, J., Jiang, Q., Shen, Y. et al. Application of recurrent neural network to mechanical fault diagnosis: a review. J Mech Sci Technol 36, 527–542 (2022). https://doi.org/10.1007/s12206-022-0102-1

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  • DOI: https://doi.org/10.1007/s12206-022-0102-1

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