摘要
针对列车混合故障的诊断,提出了一种基于集合平均经验分解(EEMD)和独立分量分析(ICA)的盲分离诊断方法。通过EEMD算法将混合信号分解为包含不同源信号特征的本征模态函数(IMF),组成新的多维信号;用主成分分析准确估计源信号个数,解决了单通道信号盲分离的欠定问题;利用快速独立分量分析(FastICA)算法实现了信号的盲分离。实验信号分别采用仿真信号和列车实验信号进行实验,实验结果表明,该算法可以有效地分离出列车的单故障信号。
Blind source separation is an effective method for multiple fault diagnosis. This paper proposed a new blind source separation algorithm based on ensemble empirical mode decomposition （EEMD） for fault diagnosis of train signal. Nonlinear mixed signal filtered wave was decomposed into intrinsic mode function （IMF） containing different source signal characteris- tics,they became the new multidimensional signals. The application of principal component analysis（PCA） could accurately es- timate the number of source signals to solve the underdetermined problem of single-channel blind signal separation. At last fast independent component analysis algorithm（FastlCA） realized the blind separation of signals. The experimental signal used the simulation signal and train mixed fault signal. Experimental results show that this algorithm can effectively analyze the charac- teristics of train single fault and has important practical value.
出处
《计算机应用研究》
CSCD
北大核心
2014年第5期1551-1553,共3页
Application Research of Computers
基金
国家自然科学基金资助项目(61134002)
关键词
盲源分离
单通道
列车故障
经验模态分解
独立分量分析
blind source separation
single channel
train fault
EMD
independent component analysis
