文献类型： 外文期刊

作者： Hou, Yuntao ¹ ; Wu, Zequan ¹ ; Cai, Xiaohua ¹ ; Dong, Zhongge ¹ ;

作者机构： 1.Heilongjiang Acad Agr Sci, Heilongjiang Acad Agr Machinery Sci, Harbin 150081, Peoples R China

关键词： DC-DC-converter circuit; soft-fault prediction; service-life estimation; support-vector machine

期刊名称：ENTROPY （ 影响因子：2.738； 五年影响因子：2.642 ）

ISSN：

年卷期： 2022 年 24 卷 3 期

页码：

收录情况： SCI

摘要： 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.