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Authors: Kaja Balzereit 1 ; Alexander Maier 1 ; Björn Barig 2 ; Tino Hutschenreuther 2 and Oliver Niggemann 3

Affiliations: 1 Fraunhofer IOSB-INA, Fraunhofer Center for Machine Learning, Langenbruch 6, Lemgo, Germany ; 2 IMMS GmbH, Ehrenbergstraße 27, Ilmenau, Germany ; 3 Fraunhofer IOSB-INA, Fraunhofer Center for Machine Learning, Langenbruch 6, Lemgo, Germany, Institute Industrial IT, OWL University of Applied Sciences, Lemgo, Germany

ISBN: 978-989-758-350-6

Keyword(s): Machine Learning, Causal Dependencies, Cyber-Physical Production Systems, Case-based Reasoning, Timed Automaton, Decision Tree Classifier, Principal Component Analysis, Data Science.

Abstract: Cyber-Physical Systems (CPS) are systems that connect physical components with software components. CPS used for production are called Cyber-Physical Production Systems (CPPS). Since the complexity of these systems can be very high, finding the cause of an error takes a lot of effort. In this paper, a data-driven approach to identify causal dependencies in cyber-physical production systems (CPPS) is presented. The approach is based on two different layers of learning algorithms: one low-level layer that processes the direct machine data and a higher-level learning layer that processes the output of the low-level layer. The low-level layer is based on different learning modules that can process differently typed data (continuous, discrete or both). The high-level learning algorithms are based on rule-based and case-based reasoning. Thus, causal dependencies are detected allowing the plant operator to find the error cause quickly.

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Paper citation in several formats:
Balzereit, K.; Maier, A.; Barig, B.; Hutschenreuther, T. and Niggemann, O. (2019). Data-driven Identification of Causal Dependencies in Cyber-Physical Production Systems.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 592-601. DOI: 10.5220/0007362005920601

@conference{icaart19,
author={Kaja Balzereit. and Alexander Maier. and Björn Barig. and Tino Hutschenreuther. and Oliver Niggemann.},
title={Data-driven Identification of Causal Dependencies in Cyber-Physical Production Systems},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={592-601},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007362005920601},
isbn={978-989-758-350-6},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Data-driven Identification of Causal Dependencies in Cyber-Physical Production Systems
SN - 978-989-758-350-6
AU - Balzereit, K.
AU - Maier, A.
AU - Barig, B.
AU - Hutschenreuther, T.
AU - Niggemann, O.
PY - 2019
SP - 592
EP - 601
DO - 10.5220/0007362005920601

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