Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Advances of digital twins for predictive maintenance

You, Yingchao, Chen, Chong, Hu, Fu, Liu, Ying ORCID: and Ji, Ze ORCID: 2022. Advances of digital twins for predictive maintenance. Presented at: 3rd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2021), Linz, Austria, 17-19 November 2021. , vol.200 Procedia Computer Science, Vol 200: pp. 1471-1480. 10.1016/j.procs.2022.01.348

[thumbnail of 1-s2.0-S187705092200357X-main.pdf]
PDF - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (728kB) | Preview


Digital twins (DT), aiming to improve the performance of physical entities by leveraging the virtual replica, have gained significant growth in recent years. Meanwhile, DT technology has been explored in different industrial sectors and on a variety of topics, e.g., predictive maintenance (PdM). In order to understand the state-of-the-art of DT in PdM, this paper focuses on the recent advances of how DT has been deployed in PdM, especially on the challenges faced and the opportunities identified. Based on the relevant research efforts recognised, we classify them into three main branches: 1) the frameworks reported for application, 2) modelling methods, and 3) interaction between the physical entity and virtual replica. We intend to analyse the techniques and applications regarding each category, and the perceived benefits of PdM from the DT paradigm are summarized. Finally, challenges of current research and opportunities for future research are discussed especially concerning the issue of framework standardisation for DT-driven PdM, needs for high-fidelity models, holistic evaluation methods, and the multi-component, multi-level model issue.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Additional Information: This is an open access article under the CC BY-NC-ND license (
Date of First Compliant Deposit: 6 October 2021
Date of Acceptance: 4 October 2021
Last Modified: 28 Jan 2024 14:36

Citation Data

Cited 16 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics