摘要
为克服小波分析中混频现象对模拟电路故障特征提取造成的不准确,提出了一种基于经验模式分解的模拟电路故障特征提取方法。该方法通过对模拟电路输出信号进行EMD得到若干个内禀模态函数,以各IMF的能量作为故障判别的特征。并针对EMD中的端点效应问题,提出了基于遗传优化的最小二乘支持向量机预测方法。仿真实验结果证明了该方法的有效性。
To overcome the imprecision of fault feature extraction for analog circuits because of the mixing phenomenon of wavelet analysis, a method of fault feature extraction for analog circuits based on empirical mode decomposition （EMD） is put forward. The method gets several intrinsic mode functions （IMF） through the EMD of analog circuit output signals, and the energy of each IMF is regard as the feature to distinguish faults. Aiming at the problem of end effect on EMD, a predictive method of least square support vector machines （LSS- VM） based on genetic optimization is put forward. The results of simulation experiment prove the validity of this method.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2009年第6期1525-1528,共4页
Systems Engineering and Electronics
关键词
模拟电路
故障特征
经验模式分解
端点效应
最小二乘支持向量机
遗传算法
analog circuit
fault feature
empirical mode decomposition
end effect
least square support vector machine
genetic algorithm
