Wavelet support vector machine for induction machine fault diagnosis based on transient current signal
This paper presents establishing intelligent system for faults detection and classification of induction motor using wavelet support vector machine (W-SVM). Support vector machines (SVM) is well known as intelligent classifier with strong generalization ability. Application of nonlinear SVM using kernel function is widely used for multi-class classification procedure. In this paper, building kernel function using wavelet will be introduced and applied for SVM multi-class classifier. Moreover, the feature vectors for training classification routine are obtained from transient current signal that preprocessed by discrete wavelet transform. In this work, principal component analysis (PCA) and kernel PCA are performed to reduce the dimension of features and to extract the useful features for classification process. Hence, a relatively new intelligent faults detection and classification method called W-SVM is established. This method is used to induction motor for faults classification based on transient current signal. The results show that the performance of classification has high accuracy based on experimental work.
wavelet support vector machines fault diagnosis transient current signal induction motor component analysis