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Open Access

Prognostics and health management for electromechanical system: A review

Dong LIUa,bJian SHIa,b( )Zirui LIAOa,b,cHaoyu GUOa,b
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Beijing Advanced Innovation Center for Big-Data Based Precision Medicine, Beihang University, Beijing 100191, China
Shen Yuan Honors College, Beihang University, Beijing 100191, China

Peer review under responsibility of Editorial Committee of JAMST

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Abstract

As a transmission component, gears take on a great significance for the Electromechanical system of aviation equipment and has long aroused the widespread attention of researchers. Fault diagnosis and remaining useful life (RUL) prediction during the gear operation is critical to prognostics and health management (PHM) of gear transmission systems. In this paper, the focus is placed on gear PHM methods. This paper attempts to review the existing methods and summarize them into four types (including physical model-based, knowledge modelbased, data-driven model-based, as well as hybrid model-based methods). Based on a wide variety of methods, the principle and the applica‐ tion situation are indicated. In particular, the data-driven model-based methods consist of stochastic algorithms, statistical algorithms, as well as the artificial intelligence (AI) method. The fault diagnosis, performance degradation and RUL prediction of various methods are primarily summarized. Furthermore, the advantages and disadvantages of various methods are assessed, and the prospect of the Digital Twin (DT) is forecasted to boost the applications of PHM.

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Journal of Advanced Manufacturing Science and Technology
Cite this article:
LIU D, SHI J, LIAO Z, et al. Prognostics and health management for electromechanical system: A review. Journal of Advanced Manufacturing Science and Technology, 2022, 2(4): 2022015. https://doi.org/10.51393/j.jamst.2022015

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Received: 01 April 2022
Revised: 15 April 2022
Accepted: 25 April 2022
Published: 15 October 2022
© 2022 JAMST

This is an Open Access article distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0),which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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