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A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning

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Abstract

The objective of this paper is to present a comprehensive review of the contemporary techniques for fault detection, diagnosis, and prognosis of rolling element bearings (REBs). Data-driven approaches, as opposed to model-based approaches, are gaining in popularity due to the availability of low-cost sensors and big data. This paper first reviews the fundamentals of prognostics and health management (PHM) techniques for REBs. A brief description of the different bearing-failure modes is given, then, the paper presents a comprehensive representation of the different health features (indexes, criteria) used for REB fault diagnostics and prognostics. Thus, the paper provides an overall platform for researchers, system engineers, and experts to select and adopt the best fit for their applications. Second, the paper provides overviews of contemporary REB PHM techniques with a specific focus on modern artificial intelligence (AI) techniques (i.e., shallow learning algorithms). Finally, deep-learning approaches for fault detection, diagnosis, and prognosis for REB are comprehensively reviewed.

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Acknowledgements

This research was supported by Korea Electric Power Corporation (R17TH02), the Basic Research Lab Program through the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT) (No. 2018R1A4A1059976), and a grant from the Institute of Advanced Machinery and Design at Seoul National University (SNU-IAMD).

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  1. Moussa Hamadache and Joon Ha Jung are co-first authors. Byeng D. Youn is the corresponding author.

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  1. Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, 08826, Republic of Korea

    Moussa Hamadache, Joon Ha Jung, Jungho Park & Byeng D. Youn

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Hamadache, M., Jung, J.H., Park, J. et al. A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning. JMST Adv. 1, 125–151 (2019). https://doi.org/10.1007/s42791-019-0016-y

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