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A survey of transfer learning for machinery diagnostics and prognostics

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Abstract

In industrial manufacturing systems, failures of machines caused by faults in their key components greatly influence operational safety and system reliability. Many data-driven methods have been developed for machinery diagnostics and prognostics. However, there lacks sufficient labeled data to train a high-performance data-driven model. Moreover, machinery datasets are usually collected from different operation conditions and mechanical components, leading to poor model generalization. To address these concerns, cross-domain transfer learning methods are applied to enhance the feasibility and accuracy of data-driven methods for machinery diagnostics and prognostics. This paper presents a comprehensive survey about how recent studies apply diverse transfer learning methods into machinery tasks including diagnostics and prognostics. Three types of commonly-used transfer methods, i.e., model and parameter transfer, feature matching and adversarial adaptation, are systematically summarized and elaborated on their main ideas, typical models and corresponding representative studies on machinery diagnostics and prognostics. In addition, ten widely-used open-source machinery datasets are presented. Based on recent research progress, this survey expounds emerging challenges and future research directions of transfer learning for industrial applications. This survey presents a systematic review of recent research with clear explanations as well as in-depth insights, thereby hel** readers better understand transfer learning for machinery diagnostics and prognostics.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 51775385 and Grant 61703279, in part by the Strategy Research Project of Artificial Intelligence Algorithms of Ministry of Education of China, in part by the Shanghai Industrial Collaborative Science and Technology Innovation Project (2021-cyxt2-kj10), in part by the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100) and the Fundamental Research Funds for the Central Universities, and in part by the Ministry of Education and King Abdulaziz University (KAU)/Deanship of Scientific Research (DSR), Jeddah, Saudi Arabia via Institutional Fund Projects under grant no. (IFPRP: 693-135-1442). We are also grateful for the efforts from our colleagues in Sino-German Center of Intelligent Systems, Tongji University.

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Authors and Affiliations

  1. Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China

    Siya Yao & Qi Kang

  2. Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 200092, China

    Siya Yao & Qi Kang

  3. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA

    MengChu Zhou

  4. Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Muhyaddin J. Rawa & Abdullah Abusorrah

  5. Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Muhyaddin J. Rawa & Abdullah Abusorrah

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  1. Siya Yao
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  2. Qi Kang
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  3. MengChu Zhou
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  4. Muhyaddin J. Rawa
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  5. Abdullah Abusorrah
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Correspondence to Qi Kang or MengChu Zhou.

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Yao, S., Kang, Q., Zhou, M. et al. A survey of transfer learning for machinery diagnostics and prognostics. Artif Intell Rev 56, 2871–2922 (2023). https://doi.org/10.1007/s10462-022-10230-4

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