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Weak fault feature extraction of rolling bearings based on globally optimized sparse coding and approximate SVD

机译：基于全局优化的稀疏编码和近似SVD的滚动轴承弱故障特征提取

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摘要

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 anl1-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.
机译：故障特征提取对于状态监测和故障预测至关重要。但是，当故障处于初始阶段时，它通常会非常微弱，并淹没在强噪声中。这使得故障特征非常难以提取。在本文中，我们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的降噪方法的启发，可以渗透到信号的底层结构中。它从噪声信号本身中学习稀疏系数和字典。该系数是基于anl1正则化最小二乘问题求解方法进行全局优化的，与传统K-SVD中应用的正交匹配追踪（OMP）相比，该方法可以更精确地定位脉冲坐标。字典学习基于奇异值分解（SVD）的近似值。使用学到的字典，我们可以捕获信号的高级结构。结合稀疏系数和学习字典，可以有效地对信号进行消噪，提取出滚​​动轴承初期的弱故障特征。说明了处理模拟信号和实验信号的结果，并均验证了所提出的方法。 SpaEIAD，小波收缩和快速峰度图也处理了所有实验数据以进行比较。

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