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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

ISBN: 978-989-8565-21-1

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.

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Paper citation in several formats:
Maier, A.; Tack, T. and Niggemann, O. (2012). Visual Anomaly Detection in Production Plants.In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8565-21-1, pages 67-75. DOI: 10.5220/0004039600670075

@conference{icinco12,
author={Alexander Maier. and Tim Tack. and Oliver Niggemann.},
title={Visual Anomaly Detection in Production Plants},
booktitle={Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2012},
pages={67-75},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004039600670075},
isbn={978-989-8565-21-1},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Visual Anomaly Detection in Production Plants
SN - 978-989-8565-21-1
AU - Maier, A.
AU - Tack, T.
AU - Niggemann, O.
PY - 2012
SP - 67
EP - 75
DO - 10.5220/0004039600670075

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