An Efficient Method to Assess Resilience and Robustness Properties of a Class of Cyber-Physical Production Systems

Widely available real-time data from the sensors of IoT infrastructure enables and increases adoption use cyber-physical production systems (CPPS) to provide enterprise-wide status information promptly respond business opportunities through monitoring, supervision control resources activities in systems. In CPPS, failures are uncertainties that inevitable unexpected. The usually lead chaos on shop floor, delayed overdue orders. This calls for development an effective method deal with CPPS. An assess impacts performance create alternative plan mitigate is important. Robustness, which refers ability tolerate perturbations, resilience, capability recover two concepts evaluate influence resource this study, we developed a CPPS based robustness resilience. We modeled by class discrete timed Petri nets. A model consists asymmetrically decomposed models tasks. dynamics tasks can be represented spatial-temporal networks (STN) similar but asymmetrical structure. joint (JSTN) constructed fusion STNs used develop efficient algorithm optimize performance. characterized resilience as properties respect resources. analyzed complexity proposed conducted experiments illustrate scalability efficiency method.