机械制造与自动化2024，Vol.53Issue(2)：220-223,273,5.DOI:10.19344/j.cnki.issn1671-5276.2024.02.046
基于优化SVM的BUCK电路故障诊断方法
Fault Diagnosis Method of BUCK Circuit Based on SVM Optimization
摘要
Abstract
As an important part of the power converter,the failure of the core power converter will directly affect the safe operation of the circuit.Therefore,this paper designs the accelerated degradation experiment of core power devices,and the electrolytic capacitor and SiC MOSFET power tube with the most serious degradation degree in the accelerated degradation experiment are adopted to represent the soft fault devices of DC-DC converter.Five working conditions are set to collect four circuit signals under each working condition.Relief algorithm is used to optimize the 48-dimensional features,particle swarm algorithm is applied to optimally support vector machine(PSO-SVM)for fault classification,and comparison by SVM and KNN classification algorithm is conducted,which verifies the superiority of the proposed method.The experimental results show that the PSO-SVM fault diagnosis method can obtain higher fault diagnosis rate.关键词
功率变换器/SiC MOSFET功率管/加速退化实验/PSO-SVMKey words
power converter/SiC MOSFET power tube/accelerated degradation test/PSO-SVM分类
信息技术与安全科学引用本文复制引用
许煜辰,王友仁,常烁..基于优化SVM的BUCK电路故障诊断方法[J].机械制造与自动化,2024,53(2):220-223,273,5.基金项目
南京航空航天大学研究生科研与实践创新计划项目(xcxjh20210329) （xcxjh20210329）
