Improving Elman Network using genetic algorithm for bearing failure diagnosis of induction motor

A bearing is an important component in any rotating machinery especially in induction motors. Thus, timely detection and diagnosis of induction motor bearing (1MB) is crucial to prevent sudden damages. This paper proposes a method to utilize artificial neural network (ANN) by using genetic algorithm (GA) to identify 1MB fault diagnosis. In this case, GA is utilized to find the optimum weights and biases for Elman Network (EN), which is one of ANN families. The vibration signal used in this work is obtained from Case Western Reserve University website. During preprocessing stage, vibration signals are been converted from time domain into frequency domain through Fast Fourier Transform (FFT). Then, enveloping method is used to eliminate the high frequency components from vibration signal. Subsequently, a set of 16 features from vibration and preprocessed signal are extracted. In order to reduce the size of data, a distance evaluation technique is used as features selection. In the development of ANN fault diagnosis, both networks EN (without GA) and GAEN (utilized with GA) in which results are compared and conclusions are drawn.