摘要
为克服噪声污染及经验模式分解(EMD)模态混叠对故障特征提取产生的不准确结果,提出了一种基于提升奇异值分解(LSVD)和集合经验模式分解(EEMD)的模拟电路故障特征提取方法.首先对模拟电路各状态下的输出信号进行提升奇异值分解去噪,消除噪声影响并实现信号局部特征的增强,然后通过EEMD得到信号的若干准确本征模态函数(IMF),最后提取各状态下EEMD能量熵作为判别的特征送入神经网络进行故障诊断.惯组中陀螺仪X轴伺服回路电路仿真实验结果证明,该方法提取的特征可以实现对故障的有效判别.
To overcome inaccurate results from fault feature extraction on analog circuits produced by noise and the mode mixing phenomenon of empirical mode decomposition （ EMD）, a new method for fault feature extraction on analog circuits based on lifting singular value decomposition （LSVD） and ensemble empirical mode de- composition （EEMD） is proposed. First, the noise in analog circuit output signals with random noise under different conditions is removed by LSVD, which eliminates noise effects and strengthens the local signal fea- tures. The processed signals are then decomposed by EEMD to extract intrinsic mode functions （IMFs）. Fi- nally, the energy entropy of each condition is calculated, which can act as the feature sent to the neural net- work for fault diagnosis. Simulation results of X-axis servo circuit failure diagnosis of the IMU （ inertial meas- urement unit） show that the fault features extracted by the proposed method can effectively diagnose all faults.
出处
《信息与控制》
CSCD
北大核心
2014年第3期293-299,共7页
Information and Control
基金
国家自然科学基金资助项目(61004128)
关键词
模拟电路
故障特征
提升小波变换
奇异值分解
集合经验模式分解
analog circuit
fault feature
lifting wavelet transform
singular value decomposi-tion
ensemble empirical modedecomposition
