Predictive Maintenance and Security Co-Design with Robotic Assembly Lines
In robotic assembly lines, mechanical degradation and cyber attack propagation are two factors that interact and affect each other on the operational stability of robotic assembly lines, which constitutes a single interconnected cyber-physical network. In this study, a Graph-Coupled Stochastic Dynamical Framework for Predictive Maintenance and Attack Propagation in Robotic Assembly Lines, based on stochastic degradation evolution and graph-coupled attack diffusion and spectral stability analysis, is presented. Robotic units are modeled as graph nodes where their combined state takes into account degradation in health status and vulnerability to security threats while considering weighted interaction dynamics. Operational variability is modeled by stochastic disturbances and adversarial spread through communication links is modeled by diffusion. Spectral propagation constraints and coupled risk evolution metrics are used to analyse the stability behaviour. Results of experimental evaluation showed that the remaining useful life error is reduced to 6.21%, attack propagation rate is reduced to 0.15, recovery time is reduced to 4.38 s and secure production continuity was improved to 96.84%, which demonstrates stable cyber-physical resilience and improved maintenance-security co-design.