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

• Autorzy: Netzer Markus , Palenga Yannic , Goennheimer Philipp , Fleischer Juergen

Identyfikatory

DOI

10.36897/jme/132248

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

data provision; anomaly detection; machine learning; manufacturing; condition monitoring

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2021
• Tom: Vol. 21, No. 1
• Strony: 98--108
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Netzer Markus

• Institute of Production Science (wbk), Karlsruhe Institute of Technology (KIT), Germany

autor

Palenga Yannic

• Institute of Production Science (wbk), Karlsruhe Institute of Technology (KIT), Germany

autor

Goennheimer Philipp

• Institute of Production Science (wbk), Karlsruhe Institute of Technology (KIT), Germany

autor

Fleischer Juergen

• Institute of Production Science (wbk), Karlsruhe Institute of Technology (KIT), Germany

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).