Prediction Method of Soft Fault and Service Life of DC‐DC‐Converter Circuit Based on Improved Support Vector Machine

Abstract

A data‐driven prediction method is proposed to predict the soft fault and estimate the service life of a DC–DC‐converter circuit. First, based on adaptive online non‐bias least‐square support‐vector machine (AONBLSSVM) and the double‐population particle‐swarm optimization (DP‐ PSO), the prediction model of the soft fault is established. After analyzing the degradation‐failure mechanisms of multiple key components and considering the influence of the co‐degradation of these components over time on the performance of the circuit, the output ripple voltage is chosen as the fault‐characteristic parameter. Finally, relying on historical output ripple voltages, the prediction model is utilized to gradually deduce the predicted values of the fault‐characteristic parameter; further, in conjunction with the circuit‐failure threshold, the soft fault and the service life of the circuit can be predicted. In the simulation experiment, (1) a time‐series prediction is made for the output ripple voltage using the model proposed herein and the online least‐square support‐vector machine (OLS‐SVM). Comparative analyses of fitting‐assessment indicators of the predicted and experimental curves confirm that our model is superior to OLS‐SVM in both modeling efficiency and prediction accuracy. (2) The effectiveness of the service life prediction method of the circuit is verified.