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Aiming at the common linear feature extraction methods can only deal with the limitation of linear data, a method of linear feature extraction extended to nonlinear by using kernel function is proposed. First extracting the amplitude frequency response of analog circuit; then due to redundancy and high domain of original signal features, the voltage features are obtained by using the Kernel Fisher Discriminator (KFD); Select SVM as the state monitor, considering the SVM parameters have great effect on the recognition rate, so using PSO to optimize SVM. The experimental results show that, the state recognition rate of this method can reach 70%, higher than the other two methods. It shows that using KFD for feature extraction and the PSO for SVM can significantly improve the recognition rate of state monitoring, reflects the new method has excellent capacity of monitoring incipient fault.