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Ensemble of Unsupervised Fuzzy C-Means classifiers for clustering health status of oil sand pumps
Di Maio F., Zio E., Pecht M., Tse P., Tsui K.L.
Dans Proceedings of the European Safety and Reliability Conference 2011 - ESREL 2011, Troyes : France (2011) - http://hal-supelec.archives-ouvertes.fr/hal-00658230
Communications avec actes
Sciences de l'ingénieur/Autre
Ensemble of Unsupervised Fuzzy C-Means classifiers for clustering health status of oil sand pumps
Francesco Di Maio () 1, Enrico Zio () 1, 2, M. Pecht 3, P. Tse 4, K.L. Tsui 4
1 :  Dipartimento di Energia
Politecnico di Milano
Italie
2 :  Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec (SSEC)
EDF – Ecole Centrale Paris – SUPELEC
France
3 :  CALCE Electronic Products and Systems Center
University of Maryland
États-Unis
4 :  Smart Engineering Asset Management Laboratory
City University of Hong Kong
Hong-Kong
Detection of anomalies and faults in slurry pumps is an important task with implications for their safe, economical, and efficient operation. Wear, caused by abrasive and erosive solid particles, is one of the main causes of failure. Condition monitoring and on-line assessment of the wear status of wetted components in slurry pumps are expected to improve maintenance management and generate significant cost savings for pump operators. In this context, the objective of the present work is to present a framework for the assessment and measurement of the wear status of slurry pumps when available data is extremely limited. Four sequential steps are performed: data collection, feature extraction, feature selection and classification. The main idea is to combine the predictions of multiple unsupervised classifiers fed with different inputs taken from different signals, based on fuzzy C-means clustering, to reduce the variance of the results so that they are less dependent on the specifics of a single classifier. This will also reduce the variance of the bias, because a combination of multiple classifiers may learn a more expressive concept class than a single classifier.
Anglais

Proceedings of the European Safety and Reliability Conference 2011
internationale
18/09/2011
419-427

ESREL 2011
18/09/2011
22/09/2011
Troyes
France

Degradation – Fault detection – Fuzzy C-means – Hierarchical Tree – Ensembles of classifiers – Slurry pumps
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