Abstract
The maintenance of critical components plays a crucial role in ensuring the overall stable operation of equipment and minimizing damages caused by functional errors. However, Traditional operation and maintenance (O&M) modes suffer from problems such as reliance on empirical judgment, lack of data support, insufficient preventive maintenance, and inadequate collaborative management. To address these issues, a viable approach is to adopt more intelligent O&M modes. Based on the characteristics of digital twin technology, such as virtual interaction and real-time feedback, a digital twin framework for critical component maintenance of equipment is proposed, providing a new approach for the practical application of digital twin in intelligent maintenance processes. This framework consists of two key components: the digital twin maintenance model and the proxy model. The process of establishing the digital twin model is elaborated in detail, and a mechanism that integrates digital twin technology and the proxy model is proposed, along with a prediction process based on the fusion of simulation and monitoring data. Finally, based on the summary of the modeling process and the proxy model, a visualization interface for intelligent maintenance of components is built using relevant engineering software.
| Original language | English |
|---|---|
| Pages (from-to) | 36064-36072 |
| Number of pages | 9 |
| Journal | IEEE Access |
| Volume | 11 |
| Early online date | 5 Apr 2023 |
| DOIs | |
| Publication status | Published - 13 Apr 2023 |
Bibliographical note
Funding Information:This work was supported in part by the Foreign Expert Project of Ministry of Science and Technology of the People's Republic of China (PRC) under Grant G2022026016L, in part by the ''ZHONGYUAN Talent Program'' under Grant ZYYCYU202012112, in part by the Henan International Joint Laboratory of Thermo-Fluid Electro Chemical System for New Energy Vehicle under Grant Yuke2020-23, in part by the Zhengzhou Measurement and Control Technology and Instrument Key Laboratory under Grant 121PYFZX181, and in part by the Fund of Innovative Education Program for Ph.D. Graduate Students with the North China University of Water Resources and Electric Power.
Publisher Copyright:
© 2013 IEEE.