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 and Germany
;
2
IMMS GmbH, Ehrenbergstraße 27, Ilmenau and Germany
;
3
Fraunhofer IOSB-INA, Fraunhofer Center for Machine Learning, Langenbruch 6, Lemgo, Germany, Institute Industrial IT, OWL University of Applied Sciences, Lemgo and Germany
Keyword(s):
Machine Learning, Causal Dependencies, Cyber-Physical Production Systems, Case-based Reasoning, Timed Automaton, Decision Tree Classifier, Principal Component Analysis, Data Science.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Case-Based Reasoning
;
Computational Intelligence
;
Enterprise Information Systems
;
Evolutionary Computing
;
Industrial Applications of AI
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Pattern Recognition
;
Soft Computing
;
Symbolic Systems
;
Theory and Methods
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.