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A systematic review of data-driven approaches to fault diagnosis and early warning

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Abstract

As an important stage of life cycle management, machinery PHM (prognostics and health management), an emerging subject in mechanical engineering, has seen a huge amount of research. Here the authors present a comprehensive overview that details previous and current efforts in PHM from an industrial big data perspective. The authors first analyze the historical development of industrial big data and its distinction from big data of other domains and summarize the sources, types, and processing modes of industrial big data. Then, the authors provide an overview of common representation and fusion (data pre-processing) methods of industrial big data. Next, the authors comprehensively review common PHM methods in the data-driven context, focusing on the application of deep learning. Finally, two industrial cases from our previous studies are included in this paper to demonstrate how the PHM technique may facilitate the manufacturing industry. Furthermore, a visual bibliography is developed for displaying current results of PHM in an appropriate theme. The bibliography is open source at “https://mango-hund.github.io/”. The authors believe that future research endeavors will require an understanding of this previous work, and our efforts in this paper will make it possible to customize and integrate PHM systems quickly for a variety of applications.

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Acknowledgements

The research is partially supported by National Key R &D Program of China (No. 2017YFE0101400), National Natural Science Foundation of China (No. 61802278), China Scholarship Council, and EU H2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement (754462). The research is also supported by the German KIT- internal research project Wertstromkinematik (Value Stream Kinematics).

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Authors and Affiliations

  1. Advanced Manufacturing Technology Center, Tongji University, Shanghai, 200092, People’s Republic of China

    Peng Jieyang & Wang Jiahai

  2. Karlsruhe Institute of Technology, 76131, Karlsruhe, Germany

    Peng Jieyang, Andreas Kimmig & Jivka Ovtcharova

  3. State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, 999078, SAR, China

    Wang Dongkun

  4. Department of Technology, Management and Economics, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark

    Xiufeng Liu

  5. College of Intelligence and Computing, Tianjin University, 300354, Tianjin, China

    Zhibin Niu

  6. Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Fraunhofer, 70569, Stuttgart, Germany

    Fan Zhi

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  1. Peng Jieyang
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  2. Andreas Kimmig
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  6. Wang Jiahai
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  7. Xiufeng Liu
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  8. Jivka Ovtcharova
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Correspondence to Zhibin Niu.

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Jieyang, P., Kimmig, A., Dongkun, W. et al. A systematic review of data-driven approaches to fault diagnosis and early warning. J Intell Manuf 34, 3277–3304 (2023). https://doi.org/10.1007/s10845-022-02020-0

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