Automatic time series segmentation and clustering for process monitoring in series production
Procedia CIRP(2023)
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.
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Key words
time series segmentation,clustering,process monitoring,semantic segmentation
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