Weak fault feature extraction of rolling bearings based on globally optimized sparse coding and approximate SVD
Weak fault feature extraction of rolling bearings based on globally optimized sparse coding and approximate SVD
复制标题
基于全局优化稀疏编码和近似SVD的滚动轴承弱故障特征提取
DOI：
10.1016/j.ymssp.2018.04.003
复制
发表时间：
2018-10-01
影响因子：
8.4
通讯作者：
Dong, Guangming
中科院分区：
文献类型：
--
作者：
Hou, Fatao;Chen, Jin;Dong, Guangming
故障特征提取是状态监测和故障预测的关键。然而，当故障处于初始阶段时，它往往很弱，淹没在强噪声中。这使得故障特征的提取非常困难。本文提出了一种基于稀疏表示理论的新方法。它受到传统的基于K-SVD的去噪方法的启发，可以深入到信号的底层结构。它从噪声信号本身学习稀疏系数和字典。基于（l1）正则化最小二乘问题求解方法对系数进行全局优化，与传统K-SVD中使用的正交匹配追踪（OMP）相比，该方法可以更准确地定位脉冲坐标。字典学习是基于奇异值分解（SVD）的近似。通过学习字典，我们可以捕获信号的高级结构。将稀疏系数与学习字典相结合，可以有效地对信号进行去噪，提取滚动轴承的早期弱故障特征。最后给出了仿真和实验信号的处理结果，验证了该方法的有效性。所有的实验数据还经过了SpaEIAD、小波收缩和快速峰图处理进行对比。(C) 2018 Elsevier Ltd.版权所有。
Fault feature extraction is crucial to condition monitoring and fault prognostics. However, when fault is in the initial stage, it is often very weak and submerged in the strong noise. This makes the fault feature very difficult to be extracted. In this paper, we propose a novel method based on sparse representation theory. It is inspired by the traditional K-SVD based de-noising method and can penetrate into the underlying structure of the signal. It learns sparse coefficients and dictionary from the noisy signal itself. The coefficients are globally optimized based on an (l1)-regularized least square problem solving method, which can locate the impulse coordinates more accurately compared with orthonormal matching pursuit (OMP) applied in the traditional K-SVD. The dictionary learning is based on an approximation of singular value decomposition (SVD). With the learned dictionary, we can capture the higher-level structure of the signal. Combining the sparse coefficients and the learned dictionary, we can de-noise the signal effectively and extract the incipient weak fault features of rolling bearings. The results of processing both simulated and experimental signals are illustrated and both validate the proposed method. All the experimental data are also processed by SpaEIAD, wavelet shrinkage, and fast kurtogram for comparison. (C) 2018 Elsevier Ltd. All rights reserved.