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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Bibliographic Details

Published in:	Journal of manufacturing systems Vol. 56; pp. 117 - 132
Main Authors:	Quatrini, Elena, Costantino, Francesco, Di Gravio, Giulio, Patriarca, Riccardo
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.07.2020
Subjects:	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 Access:	Get full text
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