Abstract:

In the process of the fault diagnosis of analog circuits,feature extraction and classifier design are two critical aspects.Most methods classify fault circuit via support vector machine (SVM) using extracted time or frequency features.In order to improve the diagnostic accuracy,we propose a new circuits fault diagnosis method based on wavelet statistical features.For a faulty circuit,we firstly emulate its time domain responses and decompose them into wavelet coefficients.Then we calculate the average,standard deviation,kurtosis,entropy and skewness of the detail coefficients before composing them into statistical feature vectors.Finally,all faulty circuits feature vectors are classified using SVM method.For optimizing the SVM parameters,cross validation is also exploited in the classification stage.Experiment results show that the proposed method is effective in the circuits fault diagnosis with an accuracy over 99%,better than that by traditional methods.

Key words: fault diagnosis;statistical features extraction;wavelet transform;support vector machine