Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors

This paper studies the application of independent component analysis (ICA) and support vector machines (SVMs) to detect and diagnose of induction motor faults. The ICA is used for feature extraction and data reduction from original features. The principal components analysis is also applied in feature extraction process for comparison with ICA does. In this paper, the training of the SVMs is carried out using the sequential minimal optimization algorithm and the strategy of multi-class SVMs-based classification is applied to perform the faults identification. Also, the performance of classification process due to the choice of kernel function is presented to show the excellent of characteristic of kernel function. Various scenarios are examined using data sets of vibration and stator current signals from experiments, and the results are compared to get the best performance of classification process.