P28 - Enhancing Predictive Maintenance with Temporal Convolutional Networks

Event
SMSI 2025
2025-05-06 - 2025-05-08
Nürnberg
Band
Poster
Chapter
Poster Session
Author(s)
F. Rieger, R. Schilling, F. Heinrich, F. Wenninger - Fraunhofer EMFT, Munich (Germany)
Pages
286 - 287
DOI
10.5162/SMSI2025/P28
ISBN
978-3-910600-06-5
Price
free

Abstract

Predictive maintenance is a crucial technique for reducing machine downtime. One challenge is the absence of labeled run-to-failure data. We propose a semi-supervised anomaly detection approach using Temporal Convolutional Networks, a regression model that has multivariate data as input and estimates vibration data. Our study reveals that our sensor signal estimation is quite accurate for normal data. The estimation error serves as a score that is useful for identifying anomalies.

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