Abstract
Cyber-physical systems are hybrid networked cyber and engineered physical elements that record data (e.g. using sensors), analyse them using connected services, influence physical processes and interact with human actors using multi-channel interfaces. Examples of CPS interacting with humans in industrial production environments are the so-called cyber-physical production systems (CPPS), where operators supervise the industrial machines, according to the human-in-the-loop paradigm. In this scenario, research challenges for implementing CPPS resilience, promptly reacting to faults, concern: (i) the complex structure of CPPS, which cannot be addressed as a monolithic system, but as a dynamic ecosystem of single CPS interacting and influencing each other; (ii) the volume, velocity and variety of data (Big Data) on which resilience is based, which call for novel methods and techniques to ensure recovery procedures; (iii) the involvement of human factors in these systems. In this paper, we address the design of resilient cyber-physical production systems (R-CPPS) in digital factories by facing these challenges. Specifically, each component of the R-CPPS is modelled as a smart machine, that is, a cyber-physical system equipped with a set of recovery services, a Sensor Data API used to collect sensor data acquired from the physical side for monitoring the component behaviour, and an operator interface for displaying detected anomalous conditions and notifying necessary recovery actions to on-field operators. A context-based mediator, at shop floor level, is in charge of ensuring resilience by gathering data from the CPPS, selecting the proper recovery actions and invoking corresponding recovery services on the target CPS. Finally, data summarisation and relevance evaluation techniques are used for supporting the identification of anomalous conditions in the presence of high volume and velocity of data collected through the Sensor Data API. The approach is validated in a food industry real case study.
Highlights
The advent of digital technologies in modern factory is leading towards a new wave of manufacturing operations, where important decisions to drive efficient and responsive production systems are made based on facts, in turn extracted from data collected from the shop floor and surrounding environment
Implementing resilience for cyber-physical production systems (CPPS) is made more difficult when on-field operators are engaged in the production process, to perform manual tasks according to the humanin-the-loop paradigm and to supervise the physical system
This paper extends the approach proposed in [3] with the following novel contributions: (i) the architectural model has been improved with the introduction of the concept of smart machine, aimed at encapsulating together recovery services, Sensor Data API and operator interface; smart machines are in turn composed into more complex structures corresponding to the whole CPPS; (ii) the context model on which the mediator is based has been enhanced, highlighting three main perspectives on product, process and smart machines; (iii) the implementation and validation of the overall architecture have been completed, by introducing the data summarisation and relevance evaluation techniques
Summary
The advent of digital technologies in modern factory is leading towards a new wave of manufacturing operations, where important decisions to drive efficient and responsive production systems are made based on facts, in turn extracted from data collected from the shop floor and surrounding environment. We propose an approach to design resilient cyber-physical production systems (R-CPPS) in the digital factory domain by addressing the three research challenges mentioned above, namely the complex structure of CPPS, the exploitation of Big Data to ensure resilience and the human-in-the-loop perspective To this aim, the approach relies on three main pillars: (i) the modelling of each component of the R-CPPS as a smart machine, that is, a cyberphysical system equipped with a set of recovery services, a Sensor Data API used to collect sensor data acquired from the physical side for monitoring the component behaviour and an operator interface for displaying detected anomalous conditions and notifying necessary recovery actions to on-field operators nearby the involved smart machine; (ii) a mediator at shop floor level, that is, in charge of ensuring resilience by gathering data from the CPPS, selecting the proper recovery actions based on the context, that is, the type of the product, the current production process phase and the involved smart machines, and invoking corresponding recovery services; (iii) data summarisation and relevance evaluation techniques, aimed at supporting the identification of anomalous conditions on data collected through the Sensor Data API, dealing with Big Data issues. Failures and disruptions on one or more components of the CPPS and on the whole production process environment are monitored through proper parameters, while service outputs are displayed nearby the component on which recovery actions must be performed
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