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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.
CitationDelgado, M. [et al.]. Accurate Bearing Faults Classification based on Statistical-Time Features, Curvilinear Component Analysis and Neural Networks. A: Annual Conference of the IEEE Industrial Electronics Society. "IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society". 2012.
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