Authors:
Alexander Maier
1
;
Tim Tack
1
and
Oliver Niggemann
2
Affiliations:
1
OWL University of Applied Sciences, Germany
;
2
OWL University of Applied Sciences and Fraunhofer IOSB-INA, Germany
Keyword(s):
Anomaly Detection, Production Plant, Automation System, Visualization Technique, Visual Analytics.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Engineering Applications
;
Enterprise Information Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Intelligent Fault Detection and Identification
;
Knowledge-Based Systems Applications
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
This paper presents a novel method for visual anomaly detection in production plants. Since the complexity of
the plants and the number of signals that have to be monitored by the operator grows, there is a need of tools
to overcome the information overflow. The human is highly able to recognize irregularities in figures. More
than 80% of the perceived information is captured visually. The approach proposed in this paper exploits this
fact and subjects data to make the operator able to find anomalies in the displayed figures. In three steps the
operator is lead from the visualization of the normal behavior over the anomaly detection and the localization
of the faulty module to the anomalous signal.