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Lookup NU author(s): Ruosen Qi,
Dr Jie ZhangORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
This paper presents an up-to-date review of data-driven condition monitoring of industrial equipment with the focus on three commonly used equipment: motors, pumps, and bearings. Firstly, the general framework of data-driven condition monitoring is discussed and the utilized mathematical and statistical approaches are introduced. The utilized techniques in recent literature are discussed. Then, fault detection, diagnosis, and prognosis on the three types of equipment are highlighted using a variety of popular shallow and deep learning models. Applications of these techniques in recent literature are summarized. Finally, some potential future challenges and research directions are presented.
Author(s): Qi R, Zhang J, Spencer K
Publication type: Article
Publication status: Published
Online publication date: 22/12/2022
Acceptance date: 19/12/2022
Date deposited: 10/01/2023
ISSN (electronic): 1999-4893
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