摘要
基于集合经验模态分解(EEMD)在信号的预处理上的不足之处,提出了一种基于自适应EEMD分解的盲源分离算法,即:先根据原始信号自身的特点确定加入白噪声的幅值标准差和集成次数,再进行EEMD分解,对所得IMF分量进行模糊综合评价以选出有效的IMF分量,构造IMF分量矩阵,最后利用盲源分离算法对其进行盲辨识,完成对信号的分解与重构。分别通过模拟信号和桥梁实测振动信号对该算法进行验证。结果表明:所提算法具有可行性,且能运用于实际结构信号的预处理。
Due to the defects of ensemble empirical mode decomposition （EEMD） in signal pretreatment, a blind source separation algorithm based on adaptive EEMD decomposition was proposed, that was： firstly, the amplitude standard deviation and integration times of the added white noise were confirmed according to the characteristics of the original signal, and then a EEMD decomposition was carried out; secondly, a fuzzy comprehensive evaluation on the obtained IMF components was carried out to select out the effective components of the IMF, and then IMF component matrix was established; finally, blind source separation algorithm was used for blind identification, and the signal decomposition and reconstruction was completed. The proposed algorithm was verified by analog signal and measuring vibration signal of bridges respectively. The results show that the proposed algorithm is feasible and can be applied to the signal preprocessing of actual structure.
出处
《重庆交通大学学报（自然科学版）》
CAS
北大核心
2016年第3期11-16,共6页
Journal of Chongqing Jiaotong University(Natural Science)
基金
浙江省教育厅科研项目(Y201432555)
浙江省住建厅科研项目(2014ZI26)
绍兴市科技计划项目(2014B70003)
关键词
桥梁工程
自适应EEMD
盲辨识
模糊综合评价法
参数识别
bridge engineering
adaptive EEMD
blind identification
fuzzy comprehensive evaluation
parameter identification
