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at - Automatisierungstechnik

Methoden und Anwendungen der Steuerungs-, Regelungs- und Informationstechnik

[AT - Automation Technology: Methods and Applications of Control, Regulation, and Information Technology
]

Editor-in-Chief: Bretthauer, Georg

12 Issues per year


IMPACT FACTOR 2016: 0.675

CiteScore 2016: 0.55

SCImago Journal Rank (SJR) 2016: 0.262
Source Normalized Impact per Paper (SNIP) 2016: 0.645

Online
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2196-677X
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Volume 65, Issue 4 (Apr 2017)

Issues

Conditional anomaly detection in event streams

Bedingte Anomalieerkennung in Ereignisströmen

Marco F. Huber
Published Online: 2017-04-12 | DOI: https://doi.org/10.1515/auto-2016-0070

Abstract

Detecting early enough the anomalous behavior of technical systems facilitates cost savings thanks to avoiding system downtimes, guiding maintenance, or improving performance. The novel framework proposed in this paper processes event streams originating from system monitoring for anomaly detection purposes. Therefore, statistical models characterizing the normal behavior of the monitored system are learned from the events. Instead of having one coarse normal model for all operational states, the proposed framework contains a mechanism for automatically detecting different conditions of the system allowing for fine-tuned models for every condition. The performance of the framework is demonstrated by means of a real-world application, where the log files of a large-scale printing machine are analyzed for anomalies.

Zusammenfassung

Die rechtzeitige Erkennung eines abweichenden Verhaltens von technischen Systemen ermöglicht Kosteneinsparungen, da Ausfälle vermieden, Wartungen zielgerichtet durchgeführt oder Leistungsparameter gesteigert werden können. Das in diesem Papier vorgestellte neuartige Framework nutzt Ereignisströme einer Prozessüberwachung zwecks der Erkennung von Anomalien. Hierzu werden statistische Modelle, welche das Normverhalten des überwachten Systems widerspiegeln, aus den Ereignisdaten gelernt. Anstelle eines einzelnen, groben Normalmodells für alle Betriebszustände, nutzt das vorgeschlagene Framework einen Mechanismus zur automatischen Erkennung verschiedener Zustände, um so für jeden Zustand ein passendes Modell bereit zu stellen. Die Leistungsfähigkeit des Frameworks wird anhand einer Realweltanwendung demonstriert, bei welcher die Logdateien einer großformatigen Druckmaschine nach Anomalien durchsucht werden.

Keywords: Anomaly detection; event processing; change point detection; machine learning; non-parametric models

Schlagwörter: Anomalieerkennung; Ereignisverarbeitung; Wechselpunkterkennung; maschinelles Lernen; nichtparametrische Modelle

About the article

Marco F. Huber

Dr. Marco Huber was born 1980 in Germany. He received his diploma degree and Ph.D. degree in computer science from the Karlsruhe Institute of Technology (KIT), Germany, in 2006 and 2009, respectively. From 2009 until 2011 he was with Fraunhofer IOSB, Karlsruhe, Germany, where he was leading a research group on computer vision and information fusion. From 2011 until 2015, he was Senior Researcher with AGT International, Darmstadt, Germany. Since 2015 he is Senior Consultant and Data Scientist with USU Software AG, Karlsruhe, as well as Adjunct Professor (Privatdozent) with the KIT. His research interests include machine learning, Big Data analytics, non-linear Bayesian estimation, probabilistic planning, and optimization.

USU Software AG, Rüppurrer Str. 1, 76137 Karlsruhe, Germany


Accepted: 2017-02-22

Received: 2016-04-16

Published Online: 2017-04-12

Published in Print: 2017-04-29


Citation Information: at - Automatisierungstechnik, ISSN (Online) 2196-677X, ISSN (Print) 0178-2312, DOI: https://doi.org/10.1515/auto-2016-0070.

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©2017 Walter de Gruyter Berlin/Boston. Copyright Clearance Center

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