A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions

Anomaly detection modeled as a one-class classification is an essential task for tool condition monitoring (TCM) when only the normal data are available. To confront with the real-world settings, it is crucial to take the different operating conditions, e.g., rotation speed, into account when approa...

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Veröffentlicht in:	IEEE access 2021, Vol.9, p.90011-90028
Hauptverfasser:	Assafo, Maryam, Langendorfer, Peter
Format:	Artikel
Sprache:	eng
Schlagworte:	Anomalies Anomaly detection Condition monitoring Data models Data structures Feature extraction feature selection Milling one-class classification Predictive maintenance Principal components analysis Self organizing maps self-organizing feature maps Task analysis TOPSIS
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