KOARCH:
Cognitive Architecture for Cyber-Physical Production Systems and Industrie 4.0

Project Duration: 1.1.2018 to 31.12.2021 (Running)
Area of Research: Industrielle Kommunikation : Artificial Intelligence in Automation
Project Manager: Prof. Dr. Henning Trsek


  

Global competition combined with increasing product complexity resulted in a massive growth in complexity of production systems in the last years. The largest part of the development content in mechanical engineering concerns software development. This increasing workload is weighing on automation specialists, system engineers and plant manufacturers.

Industrie 4.0, cyber-physical-systems and intelligent automation-systems offer a solution for this increasing burden: The main ideas is shifting human expert knowledge into automation. As opposed to the procedural approach of classical automation, the expert just formulates objectives like a description of the final product, throughout targets or the allowed energy consumption.

The knowledge refers to objectives that are described as statements and not as procedures to achieve the objectives like it was before. This means, the knowledge is described declaratively and not procedurally. This way, intelligently programed systems can use this knowledge to solve adaption and optimisation problems. Therefore, the human effort in automation decreases, e.g. in optimisation tasks, commissioning and plant modifications.

In order to realise such intelligent systems, there is a need of new automation technologies and especially new software services. This includes e.g. machine-learning-methods, condition-monitoring- and diagnosis algorithms as well as optimisation processes.

Currently these new software services get implemented in Industrie 4.0 approaches by each partner independently. The interfaces are proprietary, so that data, models and results can not be exchanged. Besides, a kognitive architecture Is developed in this project, to enable an easy exchange of data and services in Industrie 4.0 environments. Therewith, Industrie 4.0 devices and components from different manufacturers are able collaborate, i.e. they can exchange data, information (e.g. anomalies and optimisation goals) as well as algorithms and solution strategies and process them.


Technische Hochschule Köln,
Deutsche Telekom AG, Innovations Laboratories (T-Labs),
telexiom AG,
OPITZ CONSULTING Deutschland GmbH

Image: Cognitive Architecture for Cyber-Physical Production Systems and Industrie 4.0Image: Cognitive Architecture for Cyber-Physical Production Systems and Industrie 4.0Image: Cognitive Architecture for Cyber-Physical Production Systems and Industrie 4.0Image: Cognitive Architecture for Cyber-Physical Production Systems and Industrie 4.0Image: Cognitive Architecture for Cyber-Physical Production Systems and Industrie 4.0


Publications:

Bunte, Andreas; Niggemann, Oliver; Stein, Benno: Integrating OWL Ontologies for Smart Services into AutomationML and OPC UA. In: 23th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Sep 2018 (More)

Bunte, Andreas; Stein, Benno; Niggemann, Oliver: Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models. Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Hawaii, USA, Jan 2019 (More)

Bunte, Andreas; Fischbach, Andreas; Strohschein, Jan; Bartz-Beielstein, Thomas; Faeskorn-Woyke, Heide; Niggemann, Oliver: Evaluation of Cognitive Architectures for Cyber-Physical ProductionSystems. In: arXiv e-prints Feb 2019 (More)

Bunte, Andreas; Wunderlich, Paul; Moriz, Natalia; Li, Peng; Mankowski, Andre; Rogalla, Antje; Niggemann, Oliver: Why Symbolic AI is a Key Technology for Self-Adaption in the Context of CPPS. In: 24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Zaragoza, Spain, Sep 2019 (More)

Fischbach, Andreas; Strohschein, Jan; Bunte, Andreas; Stork, Jörg; Faeskorn-Woyke, Heide; Moriz, Natalia; Bartz-Beielstein, Thomas: CAAI -- A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems. In: arXiv e-prints Feb 2020 (More)

Niggemann, Oliver; Biswas, Gautam; Kinnebrew, John S.; Khorasgani, Hamed; Hranisavljevic, Nemanja; Bunte, Andreas: Konzeptualisierung als Kernfrage des Maschinellen Lernens in der Produktion. Michael ten Hompel, Birgit Vogel-Heuser, Thomas Bauernhansl (Hrsg.): Handbuch Industrie 4.0: Produktion, Automatisierung und Logistik. Springer Berlin Heidelberg , Feb 2020 (More)

Bunte, Andreas; Ressler, Henrik; Moriz, Natalia: Automated Detection of Production Cycles in Production Plants using Machine Learning. In: 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Vienna, Austria, Sep 2020 (More)

Fischbach, Andreas; Strohschein, Jan; Bunte, Andreas; Stork, Jörg; Faeskorn-Woyke, Heide; Moriz, Natalia; Bartz-Beielstein, Thomas: CAAI -- A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems. In: The International Journal of Advanced Manufacturing Technology Nov 2020 (More)

Strohschein, Jan; Fischbach, Andreas; Bunte, Andreas; Faeskorn-Woyke, Heide; Moriz, Natalia; Bartz-Beielstein, Thomas: Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems. In: arXiv e-prints Dec 2020 (More)


Funded by: Bundesministerium für Bildung und Forschung
Project Management: VDI Technologiezentrum GmbH
Grant ID: 13FH007IA6
Funding: IngenieurNachwuchs
Contact Person: Dipl.-Math. Natalia Moriz , Dr.-Ing. Andreas Bunte
Research Assistant: Dr.-Ing. Andreas Bunte , B.Sc. Philip Priss
Link: Project Homepage