组稀疏低秩矩阵估计的变转速滚动轴承故障特征提取

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (16) : 92-100.

论文

王冉1，张军武1，余亮2

作者信息 +

Group sparse low-rank matrix estimation for variable speed rolling bearing fault feature extraction

WANG Ran1，ZHANG Junwu1，YU Liang2

Author information +

文章历史 +

摘要

早期轴承故障特征的有效提取对于避免严重机械事故具有重要的意义。表征轴承故障的脉冲信号往往淹没在强背景噪声干扰中，并且轴承常常在变转速工况下运行，这使得故障特征的提取较为困难。针对这一问题，本文提出一种用于变转速工况下滚动轴承故障特征提取的组稀疏低秩矩阵估计算法。首先，根据变转速工况下轴承故障脉冲信号的角度时间循环平稳特性，利用阶频谱相关(order-frequency spectral correlation, OFSC)将测量信号转换至阶频域中。其次，揭示了轴承故障脉冲在阶频域中的组稀疏性和低秩性，并据此构建一种凸优化问题来增强这两种特性，引入非凸罚函数来提高故障特征的稀疏性。再次，在交替方向乘子法(alternating direction method of multipliers, ADMM)和优化最小化(majorization–minimization, MM)框架下求解该凸优化问题，推导出组稀疏低秩(group sparse low-rank, GSLR)矩阵估计算法。最后，通过构建增强包络阶次谱(enhanced envelope order spectrum, EEOS)对求解得到的目标分量进行故障特征检测。仿真和实验信号的分析验证了该方法在故障特征提取中的有效性。

Abstract

The effective extraction of early bearing failure features is of great importance to avoid serious mechanical accidents. The impulse signals characterizing bearing faults are often submerged in strong background noise interference, and the bearings often operate under variable speed conditions, which makes the task of fault feature extraction more difficult. To address this issue, one kind of group sparse low-rank matrix estimation algorithm for rolling bearing fault feature extraction under variable speed conditions is proposed in this paper. Firstly, the measured signal is transformed into the order-frequency domain by using order-frequency spectral correlation (OFSC) according to the angle\time cyclostationarity of the bearing fault pulse signal under variable speed conditions. Secondly, the group sparsity and low-rank property of the bearing fault pulse in the order-frequency domain are revealed, and a convex optimization problem is constructed to enhance these two properties accordingly, and a nonconvex penalty function is introduced to improve the sparsity of the fault characteristics. Again, the convex optimization problem is solved in the framework of the alternating direction method of multipliers (ADMM) and optimization-minimization (MM)，and the group sparse low-rank (GSLR) matrix estimation algorithm is derived. Finally, the target components obtained from the solution are detected by constructing the enhanced envelope order spectrum (EEOS) for the fault features. The analysis of simulation and experimental signals verify the effectiveness of the method in fault feature extraction.

导出引用

王冉1，张军武1，余亮2. 组稀疏低秩矩阵估计的变转速滚动轴承故障特征提取[J]. 振动与冲击, 2023, 42(16): 92-100

WANG Ran1，ZHANG Junwu1，YU Liang2. Group sparse low-rank matrix estimation for variable speed rolling bearing fault feature extraction[J]. Journal of Vibration and Shock, 2023, 42(16): 92-100

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