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Convolutional-Based Encoder–Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5

Schlagenhauf, Tobias 1; Wolf, Jan 1; Puchta, Alexander 1
1 Institut für Produktionstechnik (WBK), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself have not received the same attention by researchers. In this article, the authors present a publicly available multivariate time series dataset which was recorded during the milling of 16MnCr5. Due to artificially introduced, realistic anomalies in the workpiece, the dataset can be applied for anomaly detection. By using a convolutional autoencoder as a first model, good results in detecting the location of the anomalies in the workpiece were achieved. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learning. The objective of this article is to provide researchers with a real-world time series dataset of the milling process which is suitable for modern machine learning research topics such as anomaly detection and transfer learning.

Verlagsausgabe §
DOI: 10.5445/IR/1000153662
Veröffentlicht am 09.12.2022
DOI: 10.3390/data7120175
Zitationen: 1
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 12.2022
Sprache Englisch
Identifikator ISSN: 2306-5729
KITopen-ID: 1000153662
Erschienen in mdpi data
Verlag MDPI
Band 7
Heft 175
Seiten Article no: 175
Vorab online veröffentlicht am 06.12.2022
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