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Enhanced Industrial Machinery Condition Monitoring Methodology based on Novelty Detection and Multi-Modal Analysis

Autor
Cariño , J.A.; Delgado Prieto, M.; Zurita, D.; Millan, M.; Ortega, J.A.; Romero-Troncoso, R.
Tipus d'activitat
Article en revista
Revista
IEEE access
Data de publicació
2016-10-19
Volum
4
Pàgina inicial
7594
Pàgina final
7604
DOI
https://doi.org/10.1109/ACCESS.2016.2619382 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/101874 Obrir en finestra nova
URL
http://ieeexplore.ieee.org/document/7600383/ Obrir en finestra nova
Resum
This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both, the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the deman...
Citació
Cariño , J.A., Delgado Prieto, M., Zurita, D., Millan, M., Ortega, J.A., Romero-Troncoso, R. Enhanced Industrial Machinery Condition Monitoring Methodology based on Novelty Detection and Multi-Modal Analysis. "IEEE access", 19 Octubre 2016, vol. 4, p. 7594-7604.
Paraules clau
Condition Monitoring, Fault Detection, Machine Learning, Novelty Detection.
Grup de recerca
MCIA - Motion Control and Industrial Applications Research Group
PERC-UPC - Centre de Recerca d'Electrònica de Potència UPC

Participants

Arxius