Offline-Online pattern recognition for enabling time series anomaly detection on older NC machine tools
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Institute of Production Science (wbk), Karlsruhe Institute of Technology (KIT), Germany
Submission date: 2020-09-30
Final revision date: 2021-01-07
Acceptance date: 2021-01-07
Online publication date: 2021-03-29
Publication date: 2021-03-29
Corresponding author
Markus Netzer   

Institute of Production Science (wbk), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131, Karlsruhe, Germany
Journal of Machine Engineering 2021;21(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.
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