A SVDD approach of fuzzy classification for analog circuit fault diagnosis with FWT as preprocessor

In this paper, a new approach of fault diagnosis in analog circuits, which employs the Fractional Wavelet Transform (FWT) to extract fault features and adopts a fuzzy multi-classifier based on the Support Vector Data Description (SVDD) to diagnose circuit faults, is proposed. Firstly, a discrete FWT algorithm by the fractional kernel matrix is performed to preprocess fault samples. To obtain the optimal fractional order, two methods trained with the genetic algorithm are introduced. One approach is performed by the best diagnostic result, and the other is based on the maximum among-cluster center distance by the Kernel Fuzzy C-Means (KFCM) algorithm. In this paper, a threshold value is used to decrease the fuzzy region which in the overlap between hyperspheres of SVDD. Then, a SVDD fuzzy multi-classifier is applied to diagnose faults in analog circuit, and fuzzy faults are diagnosed in fuzzy sets by the relative distance. The simulation results show that the FWT succeeds in extracting local fault features and the classifier effectively detects faults.