基于PSO的模拟电路故障信息特征提取  

PSO based feature extraction method for analog circuit fault information

作　　者：刘红[1] 孙爽滋[2] 王庆元[1] 李延忠[1] 

机构地区：[1]长春理工大学光电工程学院,长春130022 [2]长春理工大学计算机学院,长春130022

出　　处：《吉林大学学报（工学版）》2015年第2期675-680,共6页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(51378076);吉林省自然科学基金项目(201115160)

摘　　要：为了提高容差电路故障诊断的诊断效率,改善电路故障征兆的特征描述,采用基于信息熵的方法生成被测容差电路的故障特征,并提出了基于离散粒子群的容差电路故障信息的特征提取方法,由此确定用于故障诊断的最佳特征子集,并以此训练故障分类器,对容差电路的软故障进行诊断定位。实验证明了基于粒子群和信息熵的特征描述和特征提取方法的有效性。In order to improve the diagnosis efficiency of faulty circuits with tolerance and to ameliorate the characteristic description of faulty symptom,first,the faulty features of the circuit with tolerance are created by information entropy method.Then,a feature extraction method based on discrete Particle Swarm Optimization（PSO）algorithm is proposed to obtain the optimal feature subset of the faulty features.Finally,the optimal feature subset is used to train the classifier to diagnose the faults of circuit with tolerance.Experiment results validate this proposed method.

关 键 词：信息处理技术 信息熵 粒子群算法 特征提取 模拟电路 故障定位 

分 类 号：TN707[电子电信—电路与系统]