Digital Twin-Driven Machine Condition Monitoring:A Literature Review

Liu, He and Xia, Min and Williams, Darren and Sun, Jianzhong and Yan, Hongsheng and Xiao, Xueliang (2022) Digital Twin-Driven Machine Condition Monitoring:A Literature Review. Journal of Sensors, 2022. ISSN 1687-725X

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Abstract

Digital twin (DT), aiming to characterise behaviors of physical entities by leveraging the virtual replica in real time, is an emerging technology and paradigm at the forefront of the Industry 4.0 revolution. The implementation of DT in predictive maintenance has facilitated its growth. As a major component of predictive maintenance, condition monitoring (CM) has great potential to combine with DT. To describe the state-of-the-art of DT-driven CM, this paper delivers a systematic review on the theoretical and practical development of DT in advancing CM. The evolution of concepts, main research areas, applied domains, and related key technologies are summarised. The driver of DT for CM is detailed in three aspects: data support, capability enhancement, and maintenance mode shift. The implementation process of DT-driven CM is introduced from the classification of DT modelling and the extension of monitoring algorithms. Finally, current challenges and opportunities for future research are discussed especially concerning the barriers and gaps in data management, high-fidelity modelling, behavior characterisation, framework standardisation, and uncertainty quantification.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Sensors
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
174314
Deposited By:
Deposited On:
08 Aug 2022 09:05
Refereed?:
Yes
Published?:
Published
Last Modified:
21 Sep 2022 00:42