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Title:
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Accurate Bearing Faults Classification based on Statistical-Time Features, Curvilinear Component Analysis and Neural Networks
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Author:
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Delgado Prieto, Miquel; Cirrincione,, Giansalvo; García Espinosa, Antonio; Ortega Redondo, Juan Antonio; Henao, Humberto
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Other authors:
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Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica; Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica; Universitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group |
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Abstract:
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Bearing faults are the commonest form of
malfunction associated with electrical machines. So far, the
research has been carried out mainly in the detection of
localized faults, but the diagnosis of distributed faults is still
under development. In this context, this work presents a new
scheme for detecting and classifying both kinds of faults. This
work deals with a new diagnosis monitoring scheme, which is
based on statistical-time features calculated from vibration
signal, curvilinear component analysis for compression and
visualization of the features behavior and a hierarchical neural
network structure for classification. The obtained results from
different operation conditions validate the effectiveness and
feasibility of the proposed methodology. |
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Abstract:
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Peer Reviewed |
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Subject(s):
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-Àrees temàtiques de la UPC::Enginyeria electrònica::Electrònica de potència
-Àrees temàtiques de la UPC::Enginyeria elèctrica
-Electric machinery
-Neural networks (Computer science)
-Fault tolerance (Engineering)
-Ball-bearings
-Màquines elèctriques
-Xarxes neuronals (Informàtica)
-Tolerància als errors (Enginyeria)
-Rodaments de boles |
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Rights:
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Document type:
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Article - Published version
Conference Object |
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