Accurate bearing faults classification based on statistical-time features, curvilinear component analysis and neural networks

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.