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An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

Jie Ren1,2, Chuqiao Xu3, Junliang Wang2,4, Jie Zhang2,*, Xinhua Mao4, Wei Shen4
1 College of Mechanical Engineering, Donghua University, Shanghai, 201620, China
2 Institute of Artificial Intelligence, Donghua University, Shanghai, 201620, China
3 School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
4 Beijing Chonglee Machinery Engineering Co., Ltd., Beijing, 101111, China
* Corresponding Author: Jie Zhang. Email:
(This article belongs to this Special Issue: Computing Methods for Industrial Artificial Intelligence)

Computer Modeling in Engineering & Sciences 2023, 135(1), 599-618. https://doi.org/10.32604/cmes.2022.022415

Received 09 March 2022; Accepted 20 May 2022; Issue published 29 September 2022

Abstract

The prognostics health management (PHM) from the systematic view is critical to the healthy continuous operation of process manufacturing systems (PMS), with different kinds of dynamic interference events. This paper proposes a three leveled digital twin model for the systematic PHM of PMSs. The unit-leveled digital twin model of each basic device unit of PMSs is constructed based on edge computing, which can provide real-time monitoring and analysis of the device status. The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters, which are deployed for the manufacturing execution on the fog server. The shop-leveled digital twin maintenance model is designed for production planning, which gives production instructions from the private industrial cloud server. To cope with the dynamic disturbances of a PMS, a big data-driven framework is proposed to control the three-level digital twin models, which contains indicator prediction, influence evaluation, and decision-making. Finally, a case study with a real chemical fiber system is introduced to illustrate the effectiveness of the digital twin model with edge-fog-cloud computing for the systematic PHM of PMSs. The result demonstrates that the three-leveled digital twin model for the systematic PHM in PMSs works well in the system's respects.

Keywords

Process manufacturing system; prognostics health management; digital twin; chemical fiber; big data-driven

Cite This Article

Ren, J., Xu, C., Wang, J., Zhang, J., Mao, X. et al. (2023). An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems. CMES-Computer Modeling in Engineering & Sciences, 135(1), 599–618.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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