%0 Journal Article
%T 基于字典学习和稀疏编码的振动信号去噪技术<br>Adaptive De-noising for Vibration Signal Based on Dictionary Learning and Sparse Coding
%A 郭亮
%A 姚磊
%A 高宏力
%A 黄海凤
%A 张筱辰
%J 振动.测试与诊断
%D 2015
%X 针对现有机械振动信号去噪算法需要一定先验知识的问题，提出了一种基于字典学习和稀疏编码的自适应去噪滤波方法。根据信号的本质特性，应用在线字典学习方法对原始数据进行学习和训练，寻求数据驱动的最优字典空间。引入正交匹配追踪算法，确定原始信号在最优字典空间上的稀疏表示。基于稀疏编码和优化字典，重构原始信号，实现信号去噪。仿真和试验结果表明，相对于现有去噪方法，基于字典学习和稀疏编码的方法自适应能力强，去噪效果好<br>While the existing de-noising algorithm requires prior knowledge of vibration signals, a new adaptive de-noising algorithm is proposed based on sparse coding and dictionary learning (DLSDF). Depending on the essential attribute of different signals, the optimal dictionary of data-driving is learned from the raw data. The orthogonal matching pursuit algorithmworks out the sparsest coefficients. Then, the de-noised signal is reconstructed using sparse coding and the optimal dictionary. Simulation and experimental results show that the algorithm based on sparse coding and dictionary learning is adaptive, and de-noising is stronger than the existing one.
%K 字典学习
%K 稀疏编码
%K 自适应滤波
%K 振动信号&lt
%K br&gt
%K dictionary learning
%K sparse coding
%K adaptive de-nosing
%K vibration signal
%U http://zdcs.nuaa.edu.cn/ch/reader/view_abstract.aspx?file_no=201504025&flag=1