From Classical to Advanced Risk Methods: Demonstrator for Industrial Cyber-Physical Systems

Modern industrial Cyber-Physical Systems (CPS) exhibit high levels of reconfigurability and heterogeneity, posing significant challenges for risk assessment in dynamic environments. Traditional risk assessment methods, originally developed for simpler systems, often fall short when dealing with the complexity of modern CPS. This paper introduces a hardware/software demonstrator designed to simulate flexible production lines, showcasing how variations in system configuration impact the balance between production costs, reliability, and safety. The demonstrator dynamically evaluates a selected system configuration using a set of risk assessment methods, including Fault Trees, Stochastic Petri Nets, and Dual Graph Error Propagation models using Probabilistic Model Checking. The demonstrator incorporates three production tasks of increasing complexity and a risk assessment method each, highlighting the strengths and limitations of each approach. Based on these findings, we propose enhancements to existing risk models. We advocate for a hybrid approach that integrates traditional and advanced methods to meet the demands of next-generation industrial systems. Our demonstrator concept can be used to evaluate how different risk assessment methods address the challenge of reconfigurability in modern industrial CPS.

Keywords: Risk assessment, Industrial cyber-physical systems, Fault tree analysis, Stochastic petri nets, Probabilistic model checking, Dual-graph error propagation model, Reconfigurable systems.