A novel method for analog circuit fault prediction based on IAALO-SVM

Abstract

As we all know, the stability of the analog circuit is an important guarantee for the normal operation of electronic equipment. During the operation of the product, once the analog circuit breaks down, it will affect the reliability and safety of the entire electronic product. Aiming at the shortcomings of the existing analog circuit prediction, combined with the characteristics of the small sample nonlinear data in the analog circuit, a fault prediction method based on Support Vector Machine (SVM) is proposed. To solve the problem that poor selection of parameters in SVM leads to poor prediction results, the Ant Lion Optimizer (ALO) algorithm is used to optimize the parameters of SVM. At the same time, the ALO algorithm is optimized to obtain the adaptive Ant Lion Optimizer (AALO) algorithm, which is used to obtain the optimized the SVM parameters. Finally, an analog circuit fault prediction method based on improved adaptive Ant Lion Optimizer-Support Vector Machine (IAALO-SVM) is proposed. In the case study, the IAALO-SVM method is used to predict the degradation and failure trends of the DC-DC switching power supply. Moreover, the prediction results of the IAALO-SVM method are compared with other algorithms such as neural networks, which demonstrates the superiority of this method in analog circuit fault prediction and can improve the accuracy and speed of electronic product fault prediction.