基于MODWPT与LFDA的模拟电路故障诊断

在模拟电路故障诊断过程中，存在故障特征信息提取不充分以及特征信息冗余的问题，对此，提出一种基于最大重叠离散小波包变换(MODWPT)与局部Fisher判别(LFDA)的模拟电路故障诊断方法。该方法中，首先利用MODWPT进行模拟电路原始信号处理与故障特征提取；随后，针对高维特征集中存在冗余信息，不利于模式识别与分类，利用LFDA方法进行降维，获取更有益于故障模式识别的低维特征集；最后，支持向量机(SVM)作为故障模式识别分类器，在此基础上构建模拟电路故障诊断模型。电路仿真实验结果表明，所提出方法的最大故障诊断准确率可达99.17%，从而验证了所提方法的有效性。

Analog circuit fault diagnosis based on MODWPT and LFDA

In the process of fault diagnosis of analog circuits, there are some problems which are insufficient extraction of fault feature information and feature information redundancy, for this, in this paper, a fault diagnosis method for analog circuits based on maximum overlapping discrete wavelet packet transform (MODWPT) and local Fisher discriminant (LFDA) is proposed. In this method, firstly, MODWPT is used to original signal processing and fault features extraction of the analog circuit. Then, due to the redundant information in the high-dimensional feature set, which is not conducive to pattern classification and recognition, LFDA is used to reduce the dimension of the high-dimensional feature set and obtain a lower-dimensional feature set which is more conducive to fault pattern recognition. Finally, support vector machine (SVM) is used as fault pattern recognition classifier, and a fault diagnosis model is constructed based on SVM. Through analog circuit simulation experiments, the maximum fault diagnosis accuracy of the proposed method can reach 99.17%, which verifies the effectiveness of the proposed method.