Anomaly detection in self-organizing industrial systems using pathlets

Marie Kiermeier, Martin Werner, Claudia Linnhoff-Popien, Horst Sauer, Jan Wieghardt

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

9 Zitate (Scopus)

Abstract

In this paper, we present a novel anomaly detection method which addresses the main challenge of self-organizing industrial systems: the state space explosion. In particular, the flexibility and dynamic nature of such systems result in an exponentially growing number of possible execution plans. To handle this problem, we propose to learn the underlying topology, instead of storing whole paths a work-piece can take through the factory. Therefore, we use the concept of pathlet learning. With it, the topology is represented by a pathlet dictionary, which contains significant sub-paths which have been extracted in a pre-processing step from a training data set. These sub-paths can then be used to evaluate at runtime the incoming trajectories. We show that with this approach we are able to detect both, global anomalous events, like the fail of a production station, as well as single anomalous trajectories, e.g. work-pieces which moves out of the known paths.

OriginalspracheEnglisch
Titel2017 IEEE International Conference on Industrial Technology, ICIT 2017
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten1226-1231
Seitenumfang6
ISBN (elektronisch)9781509053209
DOIs
PublikationsstatusVeröffentlicht - 26 Apr. 2017
Extern publiziertJa
Veranstaltung2017 IEEE International Conference on Industrial Technology, ICIT 2017 - Toronto, Kanada
Dauer: 23 März 201725 März 2017

Publikationsreihe

NameProceedings of the IEEE International Conference on Industrial Technology

Konferenz

Konferenz2017 IEEE International Conference on Industrial Technology, ICIT 2017
Land/GebietKanada
OrtToronto
Zeitraum23/03/1725/03/17

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