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A review on data-driven condition monitoring of industrial equipment

Lookup NU author(s): Dr 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.

Publication metadata

Author(s): Qi R, Zhang J, Spencer K

Publication type: Article

Publication status: Published

Journal: Algorithms

Year: 2023

Volume: 16

Issue: 1

Online publication date: 22/12/2022

Acceptance date: 19/12/2022

Date deposited: 10/01/2023

ISSN (electronic): 1999-4893

Publisher: MDPI


DOI: 10.3390/a16010009


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Funder referenceFunder name
Centre for Process Analytics and Control Technology (CPACT)