Evaluation of Cognitive Architectures for Cyber-Physical Production Systems

• Published in: 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) pp. 729 - 736
• Main Authors: Bunte, Andreas, Fischbach, Andreas, Strohschein, Jan, Bartz-Beielstein, Thomas, Faeskorn-Woyke, Heide, Niggemann, Oliver
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2019
• Subjects: Adaptation models; Cognition; Computer architecture; Concrete; Data models; Industrie 4.0; Process control; Production; Reference Architecture
• ISSN: 1946-0759
• DOI: 10.1109/ETFA.2019.8869038

Cyber-physical production systems (CPPS) integrate physical and computational resources due to increasingly available sensors and processing power. This enables the usage of data, to create additional benefit, such as condition monitoring or optimization. These capabilities can lead to cognition, such that the system is able to adapt independently to changing circumstances by learning from additional sensors information. Developing a reference architecture for the design of CPPS and standardization of machines and software interfaces is crucial to enable compatibility of data usage between different machine models and vendors. This paper analysis existing reference architecture regarding their cognitive abilities, based on requirements that are derived from three different use cases. The results from the evaluation of the reference architectures, which include two instances that stem from the field of cognitive science, reveal a gap in the applicability of the architectures regarding the generalizability and the level of abstraction. While reference architectures from the field of automation are suitable to address use case specific requirements, and do not address the general requirements, especially w.r.t. adaptability, the examples from the field of cognitive science are well usable to reach a high level of adaption and cognition. It is desirable to merge advantages of both classes of architectures to address challenges in the field of CPPS in Industrie 4.0.