Fault Diagnosis of Analog Circuit based On Wavelet Packet Analysis and SVD

In order to solve the problems of low prediction accuracy and long training time that are common in the existing analog circuit fault diagnosis models, this paper proposes a new combination of wavelet packet feature extraction, singular value decomposition(SVD) and dimensionality reduction and support vector machine(SVM) classification method. This method selects wavelet packet analysis with higher accuracy than traditional wavelet analysis, extracts features of analog circuit fault data, and normalizes the extracted feature data; then uses singular value decomposition method to perform fault data matrix decompose to achieve the purpose of dimensionality reduction. The size of the singular value obtained by decomposition reflects the characteristics of the fault information. Selecting the matrix with the largest singular value as a sample can express the fault characteristics more accurately and efficiently; finally, use the support vector machine to decompose the fault after the singular value. The matrix is trained and classified, so as to realize the fault diagnosis of the analog circuit. The simulation experiment results show that, compared with the current diagnosis models such as BAGRNN, the SVD model proposed in this paper improves the fault diagnosis rate of analog circuits, effectively reduces the amount of matrix calculation, and speeds up the diagnosis.