Effect of noise on support vector machine based fault diagnosis of IM using vibration and current signatures

Abstract

This paper analyzes the effect of noise on support vector machine (SVM) based fault diagnosis of IM (IM). For this, a number of mechanical (bearing fault, unbalanced rotor, bowed rotor and misaligned rotor) and electrical faults (broken rotor bar, stator winding fault with two severity levels and phase unbalance with two severity levels) of IM are considered here. The vibration and current signals are used here for the diagnosis. Different experiments were performed in order to generate these signals at various operating condition of IM (Speed and Load). Time domain feature are then extracted from the raw vibration and current signals obtained from the experiments. Then, the noise are added in the raw signals and the same features are extracted from this corrupted signals. The features from the original and corrupted signals are used to feed the classifier. The one-versus-one multiclass SVM are used here to perform multi-fault diagnosis. The comparative analysis of performance of the SVM classifier using data with and without noise is presented.