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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Vydané v:	Journal of manufacturing systems Ročník 56; s. 117 - 132
Hlavní autori:	Quatrini, Elena, Costantino, Francesco, Di Gravio, Giulio, Patriarca, Riccardo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.07.2020
Predmet:	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
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