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Abstract: 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. [Copyright &y& Elsevier]

In this paper, an on-line efficiency optimization controller is proposed for induction motor drives after a detailed analysis of motor losses. The controller searches for a minimum motor drive input power by continuous variation of a control variable, resulting in a smooth system performance, which is not possible by the conventional stepwise change of the control variable. The search period is short. Thus, the controller can be applied effectively to applications with frequent changes of speed and load. Also, under the proposed controller, more energy saving is achieved. Extensive simulation is performed for the induction motor under the proposed and conventional efficiency optimization controllers. The results prove the superiority of the proposed controller. [Copyright &y& Elsevier]