Reliability Modeling and Analysis of Digital Twin-Driven Cyber-Physical Manufacturing Systems

With the advancement of digitalization and intelligentization in manufacturing systems, digital twin-driven cyber-physical manufacturing systems (DT-driven CPMSs) have emerged as a key technology for enabling smart manufacturing. Existing studies have primarily focused on the applications of DT technology, but have not fully addressed the reliability challenges arising from equipment degradation and sudden failures during system operation. To address this challenge, we propose an interdependent network model for DT-driven CPMSs that integrates real-time sensing and control feedback dependencies across the cyber layer, physical layer, and virtual decision space. The model emphasizes the characterization of data dependencies between devices under sensing-control dependencies, including production data support and collaborative production dependencies. Based on the proposed system model, we further develop a system reliability model. By incorporating the routing-driven characteristics of data in the cyber layer and the material supply-demand relationships among equipment in the physical layer, the proposed reliability model enables the joint modeling of long-term equipment degradation and sudden failure propagation under sensing-control dependencies within the system. Experimental results demonstrate that the proposed model can effectively capture system reliability behavior under these challenging operational conditions. Further analysis reveals that although the cyber layer constitutes a key bottleneck for system reliability, the physical layer is more effective in regulating it. Specifically, the average gain in system reliability achieved through redundancy enhancement in the physical layer reaches 0.82, which is significantly higher than the 0.39 gain achieved in the cyber layer.