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Dumler, Jonas ; Faatz, Stephan ; Friedrich, Markus ; Döpper, Frank:
Automatic time series segmentation and clustering for process monitoring in series production.
In: Procedia CIRP.
Bd. 118
(2023)
.
- S. 602-607.
ISSN 2212-8271
DOI: https://doi.org/10.1016/j.procir.2023.06.103
Abstract
Due to high expenses for data analytics and implementation of individual process monitoring applications, potentials for data-driven process optimization often remain unused. We present a transferable method for automatic preprocessing for characteristic current and acceleration sensor signals of production plants. The method includes semi-automated segmentation, feature extraction and clustering of high sampling sensor signals. The clustered segments enable interpretation by process experts for further applications. This procedure enables low-effort preprocessing of data and allows the extraction of relevant process information from raw signals for monitoring, trend analysis and anomaly detection. Evaluation is performed on a production process for coil springs.
Weitere Angaben
| Publikationsform: | Artikel in einer Zeitschrift |
|---|---|
| Begutachteter Beitrag: | Ja |
| Institutionen der Universität: | Fakultäten > Fakultät für Ingenieurwissenschaften > Lehrstuhl Umweltgerechte Produktionstechnik > Lehrstuhl Umweltgerechte Produktionstechnik - Univ.-Prof. Dr.-Ing. Frank Döpper |
| Titel an der UBT entstanden: | Ja |
| Themengebiete aus DDC: | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften |
| Eingestellt am: | 25 Jul 2023 06:07 |
| Letzte Änderung: | 25 Jul 2023 13:46 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/86311 |