Analog Circuit Diagnosis Using Wavelet Packet And Support Vector Machine

Analog circuit fault diagnosis has been an active area since the 1960s with many significant work and methods carried out. Unfortunately, the progress of analog circuit fault diagnosis from the fundamental theory and methods to practical application has been hampered by many factors such as nonlinear effects, component tolerances, poor fault models etc. At present, the study and application of Wavelet Packet and Support Vector Machine has become the reaseach hotspot in the field of fault diagnosis. It is researched with the hope that application of Wavelet Packet and Support Vector Machine to the area of analog circuit diagnosis may achieves better results. The main purpose of this paper is to combine latest research for the Wavelet Packet and Support Vector Machine with analog circuit fault diagnosis in order to explore a new way for solving the problem of analog circuit fault diagnosis.This paper firstly gives a description for analog circuit fault diagnosis, then represents the principle of Wavelet Packet Analysis and Support Vector Machine respectively aiming at the two issues including feature extraction and fault classification of analog circuit fault diagnosis,and focuses on the application of wavelet Packet Analysis and Support Vector Machine in analog circuit fault diagnosis. Also,this paper applies the Wavelet Packet Analysis method and Least Squares Support Vector Machine method whose parameters are optimized by a method called multi-layer adaptive best-fitting parameters search respectively to clssifying the fault, so as to describe the performance of the methods by two diagnostic examples.In order to solve the difficulties in the feature extraction and classification of fault signals in analog circuits,this paper first presents a new feature extraction algorithm based on optimal Wavelet Packet combined with Fuzzy-rule.Then, a new diagnosis method combined with the feature extraction algorithm and Least Square Support Vector Machine (LSSVM) is proposed. The response signals of analog circuits are preprocessed by Wavelet Packet Transform, and the Fuzzy Rule is used to find the optimal wavelet packet coefficient of which classification capacity is better.Then, the feature set which is composed of the optimal wavelet packet energy is inputted into a LSSVM network to identify different fault case.The optimal Wavelet Packet Tranform combined with Fuzzy-rule can decrease the LSSVM network size, which is helpful to reduce algorithm complexity and accelerate learning and convergence speed. The diagnostic example illustrates this method is effective and accurate for fault location of analog circuits.