Search

An Integrated Framework for Intelligent Diagnosis and Adaptive Control of Complex Industrial Processes based on Digital Twin

Author(s)

Enpu Zhang

Affiliation(s)

Durham University England, Durham, UK

Abstract

This paper proposes an integrated framework for intelligent diagnosis and adaptive control of complex industrial processes based on digital twin technology, addressing the limitations of traditional divide-and-conquer approaches in real-time performance and system coordination. By employing multi-field coupling modeling and hybrid data-driven strategies, the framework constructs high-fidelity digital twin models that combine hierarchical fault diagnosis with multi-objective parameter self-tuning to achieve precise perception and optimized control. The study establishes a bidirectional closed-loop architecture for diagnosis and control, demonstrating the framework's stability under time-varying operating conditions and its cross-scenario adaptability. Experimental results indicate that this framework significantly enhances the autonomous decision-making capabilities of industrial systems, providing a scalable theoretical foundation and technical pathway for smart manufacturing.

Keywords

Digital Twin; Intelligent Diagnosis; Adaptive Control; Industrial Process; Integrated Framework

References

[1] Wang K,Hao H. Process industrial components intelligent fault diagnosis based on multi-sensor information interaction and multi-level feature adaptive fusion framework[J]. Measurement Science and Technology, 2025,36(10):106105-106105. [2] Shirshahi A,Moshiri B,Shoorehdeli A M. Intelligent fault diagnosis based on similarity analysis using generative model and multi-sensor fusion in industrial processes[J]. Process Safety and Environmental Protection,2025,197107097-107097. [3] Li N,Ding H,Sun X, et al. Intelligent fault diagnosis of nonlinear uncertain industrial processes based on kernel local-global interval embedding algorithm[J]. Engineering Applications of Artificial Intelligence,2025,149110486-110486. [4] Chai Tianyou, Cheng Siyu, Li Ping, et al. Intelligent Control System for Complex Industrial Processes with Edge-Cloud Collaboration [J]. Control and Decision, 2023,38(08):2051-2062. [5] Ma Zhong. Monitoring Performance of Complex Control Circuits in Industrial Processes and Intelligent Diagnosis of Actuator Failures [D]. Beijing University of Chemical Technology, 2023. [6] Wang Tong. Research on Feedback Control of Industrial Processes in Dual-Net Environment [D]. Harbin Institute of Technology, 2016. [7] Ye Haitao. Design of Fault Diagnosis and Alarm System for Complex Industrial Processes [D]. Hohai University, 2006. [8] Liu Xiaoying. Intelligent Fault Diagnosis Technology for Complex Processes and Its Application in Large Industrial Kilns [D]. Central South University, 2003. [9] Liu Bosu. Adaptive Integrated Method for Industrial Process Optimization and Control [J]. Fujian Electric Power and Electrical Engineering, 1999, (01):7-8+47.