@inproceedings{4781,
  abstract     = {{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.}},
  author       = {{Bunte, Andreas and Fischbach, Andreas and Strohschein, Jan and Bartz-Beielstein, Thomas and Faeskorn-Woyke, Heide and Niggemann, Oliver}},
  booktitle    = {{24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  isbn         = {{978-1-7281-0303-7}},
  issn         = {{1946-0759}},
  keywords     = {{Reference Architecture, Cognition, Industrie 4.0}},
  location     = {{Zaragoza, SPAIN}},
  pages        = {{729--736}},
  publisher    = {{IEEE}},
  title        = {{{Evaluation of Cognitive Architectures for Cyber-Physical Production Systems}}},
  doi          = {{10.1109/etfa.2019.8869038}},
  year         = {{2019}},
}

@misc{12826,
  abstract     = {{The configuration of current automated production systems is complex and therefore time consuming while the market demands an easy setup and adaptability due to smaller batch sizes and volatile markets. While there are different concepts in research on how to simplify the engineering process by using generic skills or capabilities of devices, run-time control is still achieved with proprietary communication protocols and commands. The concept in this paper uses skills not only in the phase of engineering but also consequently for direct and generic control of field-devices. An executable skill-metamodel therefore describes the methodological functionality which is implemented by using OPC UA due to its vendor independence as well as built-in services and information model. The implementation uses client/server-based OPC UA and the pub/sub pattern to prepare for a deterministic real-time control in conjunction with TSN, which is required by industrial automation.}},
  author       = {{Zimmermann, Patrick and Axmann, Etienne and Brandenbourger, Benjamin and Dorofeev, Kirill and Mankowski, Andre and Zanini, Paulo}},
  booktitle    = {{24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  isbn         = {{978-1-7281-0303-7}},
  issn         = {{1946-0759}},
  keywords     = {{OPC UA, Skills, Capabilities, Engineering, Field-Device, Interoperability, Control}},
  location     = {{Zaragoza, SPAIN}},
  pages        = {{1101--1108}},
  publisher    = {{IEEE}},
  title        = {{{Skill-based Engineering and Control on Field-Device-Level with OPC UA}}},
  doi          = {{10.1109/etfa.2019.8869473}},
  year         = {{2019}},
}