Machine learning for anomaly detection and process phase classification to improve safety and maintenance activities

•Define a methodology for anomaly detection with real time data from multiphase industrial process.•RFA and DJA have comparable results for the identification of process phases.•RFA has better performance than DJA for the anomaly detection.•DJA underperforms for anomalies close to the thresholds, du...

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Veröffentlicht in:	Journal of manufacturing systems Jg. 56; S. 117 - 132
Hauptverfasser:	Quatrini, Elena, Costantino, Francesco, Di Gravio, Giulio, Patriarca, Riccardo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.07.2020
Schlagworte:	Condition monitoring Condition-based maintenance Decision jungles forests Machine learning Random Condition monitoring Random forests Condition-based maintenance Decision jungles Machine learning
ISSN:	0278-6125
Online-Zugang:	Volltext
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