Data-driven anomaly detection in cyber-physical production systems

Im Zuge von Trends wie Industrie 4.0 ändert sich die Wertschöpfung in der produzierenden Industrie: Daten-basierte Services ergänzen klassische Geschäftsmodelle und schaffen neue Märkte. In diesem Artikel werden anhand des Anwendungsfalls Anomalieerkennung solche daten-basierten Services vorgestellt und diskutiert. Der Beitrag betrachtet dazu Beispiele aus der Fertigungstechnik, aus der Prozesstechnik und aus dem Gebiet der Energieanalyse.

Due to global competition and increasing product complexity, the complexity of production systems has grown significantly in recent years. This places an increasing burden on automation developers, systems engineers and plant constructors. Intelligent assistance systems and smart automation systems are a possible solution to face this complexity: The machines, i.e. the software and assistance systems, take over tasks that were previously carried out manually by experts. At the heart of this concept are intelligent anomaly detection approaches based on models of the system behaviors. Intelligent assistance systems learn these models automatically: Based on data, these systems extract most necessary knowledge about the diagnosis task. This paper outlines this data-driven approach to plant analysis using several use cases from industry.