{"id":3196,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3196"},"modified":"2022-07-26T13:06:16","modified_gmt":"2022-07-26T11:06:16","slug":"automatic-time-series-segmentation-and-clustering-for-process-monitoring-in-series-production","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/grinding-abrasive-machining\/automatic-time-series-segmentation-and-clustering-for-process-monitoring-in-series-production\/","title":{"rendered":"Automatic time series segmentation and clustering for process monitoring in series production"},"content":{"rendered":"\n

by Jonas Dumler, Stephan Faatz, Markus Friedrich, Frank Doepper (Germany)<\/em><\/p>\n\n\n\n

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Abstract<\/strong><\/p>\n\n\n\n

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.<\/p>\n\n\n\n

Keywords<\/strong>: Time series segmentation; Clustering; Process monitoring; Semantic segmentation<\/p>\n\n\n\n

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Video presentation<\/strong><\/p>\n\n\n\n