Abstract:
A prognostic model based on multi-feature vector and Adaptive Particle Swarm Optimization (APSO) optimized Support Vector Regression (SVR) is proposed to solve the difficulties in extracting fault features of analog circuits and predicting the remaining life accurately. Firstly, a multi-feature vector is constructed by combining the statistical feature and wavelet packet energy feature. Then calculating the Euclidean distance of feature vector between the initial and the degradation process to quantify the degradation state of the components in the analog circuit, the fault threshold is obtained accordingly. Finally, the fault prognostic model of SVR optimized by APSO is used for analog circuit fault prognostic, and a reference circuit is used to perform simulation experiments to verify the practicability of the method. The simulation results show that this method has a higher accuracy.