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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

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.
Originalspracheenglisch
TitelProceedings of the EDBT/ICDT 2020 Joint Conference Workshops
Untertitel3rd International Workshop on Big Data Visual Exploration and Analytics
Herausgeber (Verlag)CEUR Workshop Proceedings
Seitenumfang8
Band2578
PublikationsstatusVeröffentlicht - 2020
VeranstaltungEDBT/ICDT 2020 Joint Conference - Virtuell, Kopenhagen, Dänemark
Dauer: 30 Mär 20202 Apr 2020

Publikationsreihe

NameCEUR Workshop Proceedings
Herausgeber (Verlag)RWTH Aachen
ISSN (Print)1613-0073

Konferenz

KonferenzEDBT/ICDT 2020 Joint Conference
LandDänemark
OrtVirtuell, Kopenhagen
Zeitraum30/03/202/04/20

ASJC Scopus subject areas

  • Informatik (insg.)

Fields of Expertise

  • Information, Communication & Computing

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