A Model-Based Decision Support for Field-to-Cloud Communication of Cyber-Physical Production Systems

Abstract

Novel approaches for processing data in Cyber-Physical Production Systems (CPPS) usually require high computing capacities accessible through powerful cloud platforms like AWS or Azure. Two of the most relevant requirements to enable the use of cloud platforms in industrial automation are real-time capability and security. Addressing these requirements necessitates extensive knowledge of field-, edge-, and cloud-level technology, which is rarely available. A generalized architecture design could overcome the skill shortage in companies but conflicts with an optimized system design, which can only be attained through application- and order-specific designs. A model-based decision support is presented, enabling the definition of relevant requirements for deploying field-to-cloud communication solutions. The presented decision support combines a quality function based on system requirements from industrial automation and a domain-specific language extension, allowing automation engineers to handle the requirements necessary for the system’s design. Two representative use cases are implemented to evaluate the approach and to demonstrate its benefits.