Abstract：According to the flexibility of the fractional order p value and the detail characteristics can be extracted from the analytical signal. A new method of analog circuit two-layer fault feature extraction based on fractional Hilbert transform is proposed. The entropy of amplitude and phase and instantaneous frequency value calculated from the analytical signal are the feature values. Firstly, by using the Fisher criterion and the genetic algorithm to obtain the optimal fractional order p1 value which lead to the greatest distinction between the normal samples and other samples, extracting the features and then using the two-class SVM classifier to diagnose the normal samples. Secondly, to obtain the optimal fractional order p2 value in the same way, it makes the greatest distinction between the different classes of fault samples. Afterwards, the different fault samples are diagnosed by the multi-class SVM classifier. The experiment results show that this method can improve the performance of fault diagnosis.
罗慧, 王友仁, 崔江. 基于分数阶Hilbert变换的模拟电路双层故障特征提取方法[J]. 电工技术学报, 2010, 25(6): 150-154.
Luo Hui, Wang Youren, Cui Jiang. A Method of Analog Circuit Two-Layer Fault Feature Extraction Based on Fractional Hilbert Transform. Transactions of China Electrotechnical Society, 2010, 25(6): 150-154.
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