at Google ScholarTitle data
Dumler, Jonas ; Faatz, Stephan ; Friedrich, Markus ; Döpper, Frank:
Automatic time series segmentation and clustering for process monitoring in series production.
In: Procedia CIRP.
Vol. 118
(2023)
.
- pp. 602-607.
ISSN 2212-8271
DOI: https://doi.org/10.1016/j.procir.2023.06.103
Abstract in another language
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.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Institutions of the University: | Faculties Faculties > Faculty of Engineering Science Faculties > Faculty of Engineering Science > Chair Manufacturing and Remanufacturing Technology Faculties > Faculty of Engineering Science > Chair Manufacturing and Remanufacturing Technology > Chair Manufacturing and Remanufacturing Technology - Univ.-Prof. Dr.-Ing. Frank Döpper Research Institutions > Central research institutes > Research Center for AI in Science and Society |
| Result of work at the UBT: | Yes |
| DDC Subjects: | 600 Technology, medicine, applied sciences > 620 Engineering |
| Date Deposited: | 25 Jul 2023 06:07 |
| Last Modified: | 06 Nov 2025 07:31 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/86311 |