Model-Driven Approach for Realization of Data Collection Architectures for Cyber-Physical Systems of Systems to Lower Manual Implementation Efforts

Abstract

Data collection from distributed automated production systems is one of the main prerequisites to leverage information gain from data analysis in the context of Industrie 4.0, e.g., for the optimization of product quality. However, the realization of data collection architectures is associated with immense implementation efforts due to the heterogeneity of systems, protocols, and interfaces, as well as the multitude of involved disciplines in such projects. Therefore, this paper contributes with an approach for the model-driven generation of data collection architectures to significantly lower manual implementation efforts. Via model transformations, the corresponding source code is automatically generated from formalized models that can be created using a graphical domain-specific language. The automatically generated architecture features support for various established IIoT protocols. In a lab-scale evaluation and a unique generalized extrapolation study, the significant effort savings compared to manual programming could be quantified. In conclusion, the proposed approach can successfully mitigate the current scientific and industrial challenges to enable wide-scale access to industrial data.

An Analytics Environment Architecture for Industrial Cyber-Physical Systems Big Data Solutions

“…In the main literature review related to this topic, it has been observed that prescriptive analytics is not presented in them. For example, in [ 15 ], the model-driven proposal generates architectures for industrial data integration. In [ 10 , 16 ], a CBS adoption conceptual framework to support CPS manufacturing systems architecture for Industry 4.0 is defined.…”

“…The development of architectures of Big Data analytics that consider the different points of view in the technological merge that occurs in iCPS and allows the development of Big Data analytics platforms is another of the approaches of several researchers. For example, an architecture that collects and integrates industrial data from IIoT and manufacturing information systems (MIS) is proposed in [ 15 ] with an approach for the model-driven generation of data integration architectures to enable subsequent industrial analytics. A five-level architecture is used for CPS design in [ 10 , 16 ].…”

Counterexample Generation for Probabilistic Model Checking Micro-Scale Cyber-Physical Systems

“…Micro-scale Cyber-Physical Systems (MCPSs) are a special kind of CPS in micromachines, which are composed of micro/nanoscale components, and the integration of computation with physical processes in micro-/nano-assembly operations. MCPSs are about the intersections, not the union, of the physical and the cyber, and the behaviors of them are defined by both cyber and physical parts of the system [1][2][3]. Most of constituent elements of MCPSs or MCPSs themselves are usually accompanied with stochastic behaviors.…”

“…Up to now, existing probabilistic model checking tools cannot provide a counterexample directly. Counterexamples play a very important role in estimating or verifying MCPSs: (1) Counterexamples can feedback which parts of the system violate the requirements and provide diagnostic information; (2) Counterexamples are very effective in model-based testing, which can provide a reference for the design of test cases; (3) In the process of abstract refinement, counterexamples can provide guidance information for the refinement of rough abstract models [13,14]; (4) Counterexamples can be used to obtain the core of feasible plans in planning, such as task scheduling [15]; (5) Counterexamples are recently used to synthesize attacks for showing how confidentiality of systems can be broken, and the quality assurance of multi-agent systems [16].…”

Development of control systems and safety devices for solving the problems of digital transformation of the transport complex

“…The current global trend in the development of advanced industrial automation technologies [6,7,8] is formed in the conditions of high competition and increased business requirements for cost reduction. It is obvious that there is a need to combine at the hardware level, used on a single control object, but disparate devices and systems, into a single system that controls the entire object, with the separation of functions at the software level (SIBAS, MEN TCS, Artesyn, Hitachi, etc.)…”