Dynamicity and complexity of Cyber Physical Production Systems (CPPS) increase their vulnerability and challenge their ability to react to faults. Ensuring resilience of CPPS is even more difficult when on-field workers operating on the machines are engaged in the production line, according to the Human-in-the-Loop paradigm. In this scenario, we present a service-oriented approach to model resilient CPPS. Specifically, we design runtime recovery actions as a set of services. Services are context-based, i.e., they are associated with the steps of the production process as well as with the CPPS involved in the recovery actions. In the context model, different factors that could influence CPPS resilience, namely the functional degradation of one or more parts of the CPPS and changes in the environment in which the production process is executed, are monitored through proper parameters. The approach provides runtime selection of services among the ones associated with the process steps and CPPS involved in the recovery procedure. The values of monitored parameters are used as service inputs, while service outputs are displayed to the operators supervising the CPPS on which recovery actions must be performed, enabling fast and effective resilience also in a Human-In-the-Loop scenario. The approach is validated in a food industry case study.
Designing Context-Based Services for Resilient Cyber Physical Production Systems
Bagozi A.;Bianchini D.;De Antonellis V.
2020-01-01
Abstract
Dynamicity and complexity of Cyber Physical Production Systems (CPPS) increase their vulnerability and challenge their ability to react to faults. Ensuring resilience of CPPS is even more difficult when on-field workers operating on the machines are engaged in the production line, according to the Human-in-the-Loop paradigm. In this scenario, we present a service-oriented approach to model resilient CPPS. Specifically, we design runtime recovery actions as a set of services. Services are context-based, i.e., they are associated with the steps of the production process as well as with the CPPS involved in the recovery actions. In the context model, different factors that could influence CPPS resilience, namely the functional degradation of one or more parts of the CPPS and changes in the environment in which the production process is executed, are monitored through proper parameters. The approach provides runtime selection of services among the ones associated with the process steps and CPPS involved in the recovery procedure. The values of monitored parameters are used as service inputs, while service outputs are displayed to the operators supervising the CPPS on which recovery actions must be performed, enabling fast and effective resilience also in a Human-In-the-Loop scenario. The approach is validated in a food industry case study.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
