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Deep Transfer Learning in Mechanical Intelligent Fault Diagnosis: Application and Challenge

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Abstract

Mechanical intelligent fault diagnosis is an important method to accurately identify the health status of mechanical equipment and ensure its safe operation. With the advent of the “big data” era, it has become an inevitable tendency to choose different deep network models to improve the ability of data processing and classify faults. Meanwhile, in order to improve the generalization performances of fault diagnosis methods in different diagnosis scenarios, some fault diagnosis algorithms based on deep transfer learning have been developed. This paper introduces the concepts of deep transfer learning and explains the investigation motive. The advent in intelligent fault diagnosis of instances-based deep transfer learning, network-based deep transfer learning, mapping based deep transfer learning and adversarial-based deep transfer learning in recent years are summarized. Finally, we discuss the existing problems and development trend of deep transfer learning for intelligent fault diagnosis. This research has a positive significance for utilising deep transfer learning method in mechanical fault diagnosis.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant No. 51975394), the Natural Science Foundation of Jiangsu Province (No. BK20211336) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX20_2752). The author would appreciate the anonymous reviewers and the editor for their valuable comments.

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

    Chenhui Qian, Junjun Zhu, Yehu Shen, Quansheng Jiang & Qingkui Zhang

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  1. Chenhui Qian
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  2. Junjun Zhu
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  3. Yehu Shen
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  4. Quansheng Jiang
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  5. Qingkui Zhang
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Correspondence to Quansheng Jiang or Qingkui Zhang.

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Qian, C., Zhu, J., Shen, Y. et al. Deep Transfer Learning in Mechanical Intelligent Fault Diagnosis: Application and Challenge. Neural Process Lett 54, 2509–2531 (2022). https://doi.org/10.1007/s11063-021-10719-z

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