Session: CIE-04-01 - AMS: Uncertainty Quantification in Simulation and Model Verification & Validation

Paper Number: 89357

89357 - Digital Twin Approach to Build Predictive Maintenance Model and Its Case Study 

Predictive maintenance is considered to be an effective strategy to optimize system operation. In the execution of increasingly complex tasks, efficient and intelligent management becomes crucial. As the basis of Digital twin (DT), predictive capabilities contribute to the value of systems and help describe their complex behavior. But the challenge in Digital twin model building is still exist, it cannot accurately reproduce the physical resources, and the introduction of error will lead to the differential extension of virtual system from physical space. The challenge is how to build Digital twin capabilities and reduce accumulative error at the same time. Based on the current research progress, this paper analyzed the existing challenges in realizing predictive maintenance capability driven by Digital twin, and then, it described the predictive control process with flow path and layer framework, In addition, the way of inserting the optimization algorithm for Digital twin was explored. Finally, a practical trajectory prediction problem was taken as a case study to effectively utilize the cyclic interaction mechanism and data fusion method of Digital twin, which can consider the offset cumulative signal, and correct the prediction state in real time. This research may provide the reference for Digital twin configuration and further study.

Presenting Author: Wenqiang Yang Shanghai Jiao Tong University

Presenting Author Biography: WENQIANG YANG received the B.S. and M.S. degrees in engineering from Shanghai Jiao Tong University, Shanghai, China, in 2015 and 2018, respectively, where he is currently pursuing the Ph.D. degree in mechanical engineering. During the past few years, his research interests include Digital twin in modeling and simulation, Digital twin-driven assembly, and smart manufacturing. From 2019, he Participated in National Natural Science Foundation of China(NSFC) funding, National Key R&D Program of China, etc., which are related to the application research of Digital twin.

Authors:

Wenqiang Yang Shanghai Jiao Tong University
Xiangyu Bao Shanghai Jiao Tong University
Yu Zheng Shanghai Jiao Tong University