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The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
The forecasting model based on wavelet ν-support vector machine
Expert Systems with Applications: An International Journal
Car assembly line fault diagnosis based on robust wavelet SVC and PSO
Expert Systems with Applications: An International Journal
Fault diagnosis model based on Gaussian support vector classifier machine
Expert Systems with Applications: An International Journal
Car assembly line fault diagnosis based on modified support vector classifier machine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A self-adaptive embedded chaotic particle swarm optimization for parameters selection of Wv-SVM
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An intelligent forecasting model based on robust wavelet ν-support vector machine
Expert Systems with Applications: An International Journal
SVM practical industrial application for mechanical faults diagnostic
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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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.