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A generic data fusion and analysis platform for cyber-physical systems

: Kühnert, Christian; Montalvo Arango, I.


Beyerer, Jürgen (Ed.); Niggemann, Oliver (Ed.); Kühnert, Christian (Ed.):
Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2016, held at the Fraunhofer IOSB in Karlsruhe, September 29th, 2016
Berlin: Springer Vieweg, 2017 (Technologien für die intelligente Automation 3)
ISBN: 978-3-662-53805-0 (Print)
ISBN: 978-3-662-53806-7
Conference on Machine Learning for Cyber-Physical-Systems and Industry 4.0 (ML4CPS) <2, 2016, Karlsruhe>
Conference Paper
Fraunhofer IOSB ()
industry 4.0; Condition-Monitoring; Plug-in architecture; data fusion; data analysis; Cyber-Physical Systems; Generic Adaptable

In the future, production systems and information technology will merge, providing new ways for data processing and analysis. Still, the current situation is that for different production environments, different IT infrastructures exist. This makes data gathering, fusion and analysis process an elaborate work or even unfeasible.
Hence, this paper presents a generic, extendable and adaptable data fusion and analysis platform. Within this platform it is possible to connect onto different production systems, collect and process their measurements in realtime and finally give feed-back to the user. To keep the platform generic, the architecture follows a plug-in based approach. It is possible to integrate data from new productions systems into the platform as well as tailor made algorithms for analysis. As a use case, the platform is used on an industry 4.0 testbed which is used to monitor and track the lifecycle of a load process.