Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Data-driven Identification of Causal Dependencies in Cyber-Physical Production Systems

: Balzereit, Kaja; Maier, Alexander; Barig, Björn; Hutschenreuther, Tino; Niggemann, Oliver

Volltext urn:nbn:de:0011-n-5409156 (1.0 MByte PDF)
MD5 Fingerprint: 6dc4a0a29edf6d4a2480b21b867e3f7c
Erstellt am: 25.4.2019

Rocha, A. ; Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
11th International Conference on Agents and Artificial Intelligence, ICAART 2019. Proceedings. Vol.2 : February 19-21, 2019, Prague, Czech Republic
SciTePress, 2019
ISBN: 978-989-758-350-6
International Conference on Agents and Artificial Intelligence (ICAART) <11, 2019, Prague>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
machine learning; causal dependencies; cyber-physical production systems; case-based reasoning; decision tree classifier; principal component analysis; Data Science

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.