动态模式分解及其在轴承早期故障诊断中的应用

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (12) : 313-320.

论文

文明1,2，党章1,2,3，余震1,2，吕勇1,2魏国前1,2

作者信息 +

Dynamic mode decomposition and its application in early bearing fault diagnosis

WEN Ming1,2, DANG Zhang1,2,3, YU Zhen1,2, L Yong1,2， WEI Guoqian1,2

Author information +

文章历史 +

摘要

滚动轴承是装备制造业中关键基础零部件，其在服役过程中的工作状态直接决定着主机产品的性能、质量和可靠性，有效的轴承早期故障监督检测技术是实现设备预防维修的重要保证。提出将动态模式分解(DMD)算法改进后应用于轴承早期故障信号的特征提取中，传感器采集得到的原始时间序列通过投影降噪后进行动态模式分解，采用软阈值法自适应地根据原始信号噪声水平得到若干单频模式，对这些模式分量进行多尺度排列熵值计算，通过阈值法筛选出原始动力学系统中的低秩成分，对其进行恢复重构后提取早期故障特征频率。在仿真信号和实测信号的实验研究中验证了本文提出方法的有效性。

Abstract

Rolling bearing is a key component in the equipment manufacturing industry, its working status in the service process directly determines the performance, quality and reliability of the host products. Effective supervision and detection technology for early fault of bearing is an important guarantee for the equipment preventive maintenance. An improved dynamic mode decomposition (DMD) algorithm was proposed and applied to early fault extraction of bearing signals. The original time series collected by the sensor was decomposed by DMD after projection denoising, then the soft threshold method was adopted to obtain several single-frequency modes adaptively according to the noise level of original signal. Multi-scale permutation entropy values of these modes were calculated next, the low-rank components in the original dynamic system were picked out by the threshold method. After the reconstruction, the early fault frequencies were extracted. The effectiveness of the proposed algorithm was verified with experimental study on simulation signal and measured signal.

导出引用

文明1,2，党章1,2,3，余震1,2，吕勇1,2魏国前1,2. 动态模式分解及其在轴承早期故障诊断中的应用[J]. 振动与冲击, 2022, 41(12): 313-320

WEN Ming1,2, DANG Zhang1,2,3, YU Zhen1,2, L Yong1,2， WEI Guoqian1,2. Dynamic mode decomposition and its application in early bearing fault diagnosis[J]. Journal of Vibration and Shock, 2022, 41(12): 313-320

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