A New Family of Model-Based Impulsive Wavelets and Their Sparse Representation for Rolling Bearing Fault Diagnosis
A New Family of Model-Based Impulsive Wavelets and Their Sparse Representation for Rolling Bearing Fault Diagnosis
复制标题
用于滚动轴承故障诊断的一类新的基于模型的脉冲小波及其稀疏表示
DOI：
10.1109/tie.2017.2736510
复制
发表时间：
2018-03-01
影响因子：
7.7
通讯作者：
Qin, Yi
中科院分区：
文献类型：
--
作者：
Qin, Yi
通过提取滚动轴承的脉冲特征，可以对滚动轴承的局部故障进行诊断。然而，近似周期性的脉冲可能被淹没在由其他组件和背景噪声产生的强干扰中。针对这一问题，本文提出了一种基于稀疏表示的脉冲特征提取方法。根据轴承故障产生的脉冲振动模型，构造了一种新的满足容许性条件的脉冲小波。因此，这一系列基于模型的脉冲小波可以形成一个Parseval框架。利用基于模型的脉冲小波基和傅立叶基，构造了一个凸优化问题来提取重复脉冲。基于分裂思想，提出了一种迭代阈值收缩算法来解决该问题，该算法具有较快的收敛速度。通过仿真信号和含有轴承故障信息的真实的振动信号，验证了该方法的重复脉冲特征提取性能，并与经典谱峰度法、基于模拟退火的优化谱峰度法和基于共振的信号分解法进行了比较。实验结果表明了该方法在弱重复瞬态特征提取中的优势和优越性。
The localized faults of rolling bearings can be diagnosed by the extraction of the impulsive feature. However, the approximately periodic impulses may be submerged in strong interferences generated by other components and the background noise. To address this issue, this paper explores a new impulsive feature extraction method based on the sparse representation. According to the vibration model of an impulse generated by the bearing fault, a novel impulsive wavelet is constructed, which satisfies the admissibility condition. As a result, this family of model-based impulsive wavelets can form a Parseval frame. With the model-based impulsive wavelet basis and Fourier basis, a convex optimization problem is formulated to extract the repetitive impulses. Based on the splitting idea, an iterative thresholding shrinkage algorithm is proposed to solve this problem, and it has a fast convergence rate. Via the simulated signal and real vibration signals with bearing fault information, the performance of the proposed approach for repetitive impulsive feature extraction is validated and compared with the noted spectral kurtosis method, the optimized spectral kurtosis method based on simulated annealing, and the resonance-based signal decomposition method. The results demonstrate its advantage and superiority in weak repetitive transient feature extraction.