Development of artificial neural network based fault diagnosis of induction motor dearing

Abstract

The common component failure of induction motor is bearing. Thus, timely detection and diagnosis of induction motor bearing (IMB) is very crucial in order to prevent sudden damage. This paper proposes developing artificial neural network (ANN) model of IMB fault diagnosis by using Elman Network. The vibration signal obtained from Case Western Reserve University website are been used as input signal. During preprocessing stage, vibration signal have been converted from time domain into frequency domain through fast Fourier transform (FFT). Enveloping method was then, used to eliminate the high frequency components from vibration signal. Subsequently, a set of 16 features from time and frequency domain were extracted. Furthermore, the distance evaluation technique is used in features selection in order to select only informative features. In order to make the ANN model more flexible, the sensitivity analysis of IMB is introduced. Lastly, a graphical user interface (GUI) program is created as a tool for help users determines the situation of IMB conditions.