Studies related with the induction motor bearings fault detection have been used digital signal processing and pattern recognition techniques. However, performance of these approaches depends on the use of correct features. This paper deals an analysis of the use of filter banks with uniform and nonuniform frequency subbands to features extraction from vibration signals. Discrimination was developed by an artificial neural network with feedforward connections. Results identifies that the employment of filter banks improve the accuracy in 23percent-flag-change for six considered classes related with faults in bearings.
