基于字典学习和稀疏编码的振动信号去噪技术
Adaptive De-noising for Vibration Signal Based on Dictionary Learning and Sparse Coding

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
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.