In modern Cyber Physical Production Systems (CPPS) workers interact with hybrid networked cyber and engineered physical elements that record data (e.g., using sensors), analyse them using connected services and support decision making, according to the Human-In-the-Loop paradigm. In this paper we present an approach for modelling Resilient Cyber Physical Production Systems (R-CPPS). The approach is conceived as: (i) data-driven, because recovery actions, modelled in a service-oriented architecture, are activated by sensor data measures collected on the CPPS subsystems and the surrounding production environment; (ii) context-based, since recovery services are associated with the steps of the production process as well as with the hierarchical organisation of the CPPS components involved in the recovery actions. The approach provides runtime selection of services, where the sensor data measures are used as service inputs and service outputs are displayed to the operators who supervise the CPPS subsystem on which recovery actions must be performed, enabling fast and effective resilience also in a Human-In-the-Loop scenario. The feasibility of the approach is demonstrated in a food industry case study.
A data-driven context-based approach for modelling Resilient Cyber Physical Production Systems
Bagozi A.;Bianchini D.;de Antonellis V.
2021-01-01
Abstract
In modern Cyber Physical Production Systems (CPPS) workers interact with hybrid networked cyber and engineered physical elements that record data (e.g., using sensors), analyse them using connected services and support decision making, according to the Human-In-the-Loop paradigm. In this paper we present an approach for modelling Resilient Cyber Physical Production Systems (R-CPPS). The approach is conceived as: (i) data-driven, because recovery actions, modelled in a service-oriented architecture, are activated by sensor data measures collected on the CPPS subsystems and the surrounding production environment; (ii) context-based, since recovery services are associated with the steps of the production process as well as with the hierarchical organisation of the CPPS components involved in the recovery actions. The approach provides runtime selection of services, where the sensor data measures are used as service inputs and service outputs are displayed to the operators who supervise the CPPS subsystem on which recovery actions must be performed, enabling fast and effective resilience also in a Human-In-the-Loop scenario. The feasibility of the approach is demonstrated in a food industry case study.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
