基于改进稀疏编码的微弱振动信号特征提取算法

针对强噪声环境下难以有效提取微弱振动信号特征的问题,提出了基于改进字典学习和移不变分量过滤(IDL-SICF)的稀疏编码振动信号特征提取算法。首先,将振动信号进行分段和平滑预处理以降低数据处理复杂度,接着利用改进的字典学习和高效系数求解算法构建基于移不变稀疏编码的自适应滤波器,然后过滤字典原子重构的移不变分量以获得表征信号本质特征的最优基函数,取最优基函数对应的移不变分量的特征频率强度作为评价信号特征提取效果的优劣。仿真和实测数据的试验结果表明,相比于现有微弱振动信号提取算法,提出的算法具有更强的特征提取能力,在实际应用中具有较高的可行性。

Feature extraction of weak vibration signal based on improved sparse coding

The feature extraction is difficult to conduct for weak vibration signal in strong noise background. Thus, a feature extraction algorithm is proposed based on Improved Dictionary Learning and Shift Invariant Component Filtering(IDL SICF). Firstly, vibration signal is segmented and smoothed to decrease the complexity. Then, improved dictionary learning algorithm as well as efficient coefficient solver is used for constructing adaptive filter based on shift invariant sparse coding. The shift invariant components constructed by dictionary atoms is filtered to obtain optimal basis function for representing inherent signal features. Finally, intensity of characteristic frequency in optimal basis function is utilized for evaluating performance in signal feature extraction. Experiments on both simulation data and practical data demonstrate that the proposed algorithm can realize better performance on feature extraction compared with the up to date methods and is more feasible for the practical applications.