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Offline-online pattern recognition for enabling time series anomaly detection on older NC machine tools

Netzer Markus, Palenga Yannic, Goennheimer Philipp, Fleischer Juergen

Journal of Machine Engineering

2021

Vol. 21, No. 1

98--108

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.