Intelligent IoT functions for increased availability, productivity and component quality offer significant added value to the industry. Unfortunately, many old machines and systems are characterized by insufficient, inconsistent IoT connectivity and heterogeneous parameter naming. Furthermore, the data is only available in unstructured form. In the following, a new approach for standardizing information models from existing plants with machine learning methods is described and an offline-online pattern recognition system for enabling anomaly detection under varying machine conditions is introduced. The system can enable the local calculation of signal thresholds that allow more granular anomaly detection than using only single indexing and aims to improve the detection of anomalous machine behaviour especially in finish machining.
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Machine tool process monitoring by segmented timeseries anomaly detection using subprocess-specific thresholds Markus Netzer, Yannic Palenga, Jürgen Fleischer Production Engineering
Datenaufnahme und -verarbeitung in der Brownfield-Produktion Philipp Gönnheimer, Markus Netzer, Carolin Lange, Roman Dörflinger, Judith Armbruster, Jürgen Fleischer Zeitschrift für wirtschaftlichen Fabrikbetrieb
Monitoring of Tool and Component Wear for Self-Adaptive Digital Twins: A Multi-Stage Approach through Anomaly Detection and Wear Cycle Analysis Robin Ströbel, Alexander Bott, Andreas Wortmann, Jürgen Fleischer Machines
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