Exploration of Anomalies in Cyclic Multivariate Industrial Time Series Data for Condition Monitoring

J. Suschnigg, B. Mutlu, A. Fuchs, V. Sabol, S. Thalmann, T. Schreck

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Industrial product testing is frequently performed in cycles, resulting in cycle-dependent test data. Monitoring the condition of products under test involves analysis of large and complex test data sets. Main tasks are to detect anomalies and dependencies between observation variables, which appears to be challenging to engineers. In this paper, we present a flexible and extendable visual analytics approach for anomaly detection focusing on cycle-depended data. It is based on a glyph representation to visualize anomaly scores of cycles with respect to interactively selected reference data. Our approach is built on a design study in collaboration with an industrial engineering corporation, and is demonstrated on real data from engines tested on automotive testbeds. Based on findings from evaluation results, we provide a discussion and an outlook for future work.
Original languageEnglish
Title of host publicationProceedings of the EDBT/ICDT 2020 Joint Conference Workshops
Subtitle of host publication3rd International Workshop on Big Data Visual Exploration and Analytics
PublisherCEUR Workshop Proceedings
Number of pages8
Publication statusPublished - 2020
EventEDBT/ICDT 2020 Joint Conference - Virtuell, Kopenhagen, Denmark
Duration: 30 Mar 20202 Apr 2020

Publication series

NameCEUR Workshop Proceedings
PublisherRWTH Aachen
ISSN (Print)1613-0073


ConferenceEDBT/ICDT 2020 Joint Conference
CityVirtuell, Kopenhagen

ASJC Scopus subject areas

  • Computer Science(all)

Fields of Expertise

  • Information, Communication & Computing


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