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Data-driven monitoring of cyber-physical systems leveraging on big data and the internet-of-things for diagnosis and contro

: Niggemann, O.; Biswas, G.; Kinnebrew, J.S.; Khorasgani, H.; Volgmann, S.; Bunte, A.

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Pencolé, Yannick (Ed.); Travé-Massuyès, Louise (Ed.); Dague, Philippe (Ed.):
26th International Workshop on Principles of Diagnosis, DX 2015. Proceedings. Online resource : August 31-September 3, 2015, Paris, France; Co-located with 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, Safeprocess 2015
Paris, 2015 (CEUR Workshop Proceedings 1507)
URN: urn:nbn:de:0074-1507-1
International Workshop on Principles of Diagnosis (DX) <26, 2015, Paris>
Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) <9, 2015, Paris>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

The majority of projects dealing with monitoring and diagnosis of Cyber Physical Systems (CPSs) relies on models created by human experts. But these models are rarely available, are hard to verify and to maintain and are often incomplete. Data-driven approaches are a promising alternative: They leverage on the large amount of data which is collected nowadays in CPSs, this data is then used to learn the necessary models automatically. For this, several challenges have to be tackled, such as real-time data acquisition and storage solutions, data analysis and machine learning algorithms, task specific human-machine-interfaces (HMI) and feedback/control mechanisms. In this paper, we propose a cognitive reference architecture which addresses these challenges. This reference architecture should both ease the reuse of algorithms and support scientific discussions by providing a comparison schema. Use cases from different industries are outlined and support the correctness of the architecture.