Fault diagnosis for rolling bearings under unknown time-varying speed conditions with sparse representation
Fault diagnosis for rolling bearings under unknown time-varying speed conditions with sparse representation
复制标题
未知时变速度条件下滚动轴承的稀疏表示故障诊断
DOI：
10.1016/j.jsv.2020.115854
复制
发表时间：
2021-03-03
影响因子：
4.7
通讯作者：
Dong, Guangming
中科院分区：
文献类型：
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作者：
Hou, Fatao;Selesnick, Ivan;Dong, Guangming
在实际应用中，轴承经常以时变速度运行，这会引起非平稳的振动信号。如何在未知的变速工况下有效地提取故障特征频率是一项具有挑战性的工作。提出了一种不需要速度信息的稀疏时频故障诊断方法.首先，利用希尔伯特变换对振动信号进行解调。然后采用迭代软阈值算法求解1范数正则化线性最小二乘代价函数。详细阐述了FCF的稀疏性，以及如何选择基将振动信号映射到稀疏空间。在适当的基元下，该解即为优化的稀疏时频域重构，它可以大大提高信号的时频分辨率，同时有效地去除信号中的噪声。该方法在轴承健康状态下不产生混淆成分，而在轴承有缺陷时则显示FCF。为了证明该方法的鲁棒性，在各种时变工况下，用仿真和实验信号对该方法进行了验证。并分别用短时傅里叶变换、基于傅里叶的同步压缩变换和脊线提取方法对信号进行了处理。(C)2020爱思唯尔有限公司保留所有权利。
In practice, bearings often run at a time-varying speed, which induces non-stationary vibration signals. How to extract the fault characteristic frequency (FCF) effectively under unknown variable speed conditions is a challenging work. This paper proposes a sparse time frequency method for fault diagnosis with no speed information demanded. Firstly, the Hilbert Transform is used to demodulate the vibration signal. Then the iterated soft-thresholding algorithm is applied to solve the 1 norm regularized linear least squares cost function. The sparsity of the FCF is expounded in detail, and how to choose the basis to map the vibration signal into the sparse space is also detailed. With the appropriate basis, the solution is exactly the optimized sparse TFR, which can enhance both time and frequency resolutions greatly, and meanwhile denoise the signal effectively. This method does not produce the confusing components when the bearing is in the healthy condition, while indicates the FCF when the bearing is defective. To show the robustness of the effectiveness, the proposed method is verified with simulated and experimental signals under various time-varying operating conditions. All the signals are also processed with the STFT, Fourier-based Synchrosqueezing Transform and ridge extraction method for comparison. (C) 2020 Elsevier Ltd. All rights reserved.