模拟电路故障特征降维方法  

Dimensionality Reduction Method of Analog Circuit Fault Features

作　　者：廖剑[1,2] 周绍磊[2] 史贤俊[2] 王朕[2] 

机构地区：[1]中国人民解放军第91550部队 [2]海军航空工程学院控制工程系

出　　处：《振动．测试与诊断》2015年第2期302-308,400,共7页Journal of Vibration,Measurement & Diagnosis

基　　金：国家青年科学基金资助项目(61203168)

摘　　要：为有效进行模拟电路故障特征的降维处理,在线性判别分析中通过引入局部化思想提出一种局部线性判别分析降维方法。首先,构造单个数据的局部数据块,计算其类内、类间散度矩阵,通过缩放因子平衡局部邻域类内紧性和类间散性之差建立单个数据的局部优化准则;然后,在整个数据空间中采用对齐算法重构最终目标函数,最后使用标准特征值分解方法求得投影矩阵完成数据降维。算法充分利用数据的局部判别信息使其能够处理数据的非线性并保持数据的类区分度,而且克服了线性判别分析中的小样本问题。算法在标准数据集和模拟电路故障特征提取中进行实验均取得了较好的效果。To help solve the dimension reduction of the ault features in analog circuits,a method called local linear discrimination analysis(LLDA)is proposed by introducing the idea of localization in linear discrimination analysis(LDA).A local data patch of a single group of data is constructed,and the scatter matrices of the intra-class and inter-class are computed.Next,the local optimization criterion is established by balancing the difference between the intra-class compactness and inter-class separation by the scaling factor.Then,theobjective function is reconstructed on the alignment algorithm in the whole data space.Finally,the projection matrix is obtained by conducting the standard eigenvalue decomposition for dimension reduction.The algorithm makes full use of the local discrimination information to solve the nonlinearity of the data while maintaining the class differentiation,and avoids the problem of small samples in LDA.Studies on the standard dataset and extraction of fault features in the analog circuits demonstrate the effectiveness of LLDA.

关 键 词：降维 模拟电路 特征提取 线性判别分析 局部线性判别分析 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TP206.1[自动化与计算机技术—计算机科学与技术]