Induction motor fault diagnosis using multiple class feature selection

This paper presents an effective and practical multiple class feature selection (MCFS) approach for induction motor fault diagnosis. Wavelet transform is applied to extracting energy features at some specific frequency components from both stator current signals and vibration signals. These energy features are collected to form a high-dimensional feature vector. MCFS algorithm is then introduced to select representative ones from the feature vector and used as input to a random forest classifier for induction motor fault pattern recognition. Experimental study performed on a machine fault simulator indicates that the MCFS can be used as an effective algorithm for feature dimension reduction in the field of induction motor fault diagnosis.