Energy eigenvalues and neural network analysis for broken bars fault diagnosis in induction machine under variable load: experimental study

Abstract

In this paper, a technique primarily based on the discrete wavelet transform (DWT) and the slip associated with a neural network (NN) for classification and fault detection of broken rotor bars in an induction machine has been proposed. The calculated energy in each decomposition level obtained by the DWT analysis and the slip of the motor are used as input to the classifier in order to diagnose the healthy and faulty classes. The advantage of this method lays in the use of one single current sensor with the slip factor in order to detect the presence of the fault and identify the number of broken bars under different load conditions. The DWT analysis is proposed to overcome the limitation of the Fourier analysis and it is mainly adapted for the non-stationary signals. Moreover, the slip factor is applied as a second input on the classifier of NN to eliminate the problem of low-load. Feed-forward multi-layer Perceptron neural network is chosen as a technique to classify the fault in an induction machine. This technique is performed and validated by experimental data.