基于信号子空间的新型盲解卷积方法

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (3) : 139-147.

论文

基于信号子空间的新型盲解卷积方法

周涛1,2，赵明1，郭栋2，欧曙东1

作者信息 +

A new blind deconvolution method based on signal subspace

ZHOU Tao1,2, ZHAO Ming1, GUO Dong2, OU Shudong1

Author information +

文章历史 +

摘要

解卷积方法已广泛应用于振动信号的故障冲击提取。然而设备运行工况复杂多变、故障特征周期难以准确预知以及随机冲击干扰，使得当前的解卷积方法难以适应工业现场复杂环境下故障冲击增强的需求。针对上述问题，提出了一种基于信号子空间的新型盲解卷积方法。该方法通过奇异值分解(SVD)方法将测试信号空间分解，分离各子空间，在此基础上通过稀疏编码收缩抑制子空间噪声，以脉冲稀疏指数为指标筛选有效子空间，最后迭代实现故障脉冲提取。轴承变转速仿真实验和列车轴承实验结果表明，该方法不仅可以有效消除随机冲击和噪声，避免能量对子空间筛选的影响,而且在缺乏准确的故障特征周期情况下仍能实现故障脉冲的准确提取。

Abstract

The deconvolution methods have been widely applied to extract the fault impulse from the vibration signal. However, due to the complex and changeable operating conditions of the equipment, the difficulty in accurately predicting the fault feature period and the interference of random impulse, the current deconvolution method is difficult to meet the requirements of the enhanced fault impulse in the complex environment of the industrial site. To solve the above problem, a blind deconvolution method based on signal subspace was proposed. The method decomposed the test signal space and separated each subspace by singular value decomposition (SVD), restrained subspace noise by sparse code shrinkage. Then effective subspace was screened based on impulse sparse index. Finally fault impulse was extracted by iteration. The experimental results of bearing simulation signals in variable speed and train bearing show that proposed method can effectively eliminate the interference of random impulse and noise, avoid the influence of energy on subspace screening, and accurately extract fault impulse without the precise fault feature period.

导出引用

周涛1,2，赵明1，郭栋2，欧曙东1. 基于信号子空间的新型盲解卷积方法[J]. 振动与冲击, 2022, 41(3): 139-147

ZHOU Tao1,2, ZHAO Ming1, GUO Dong2, OU Shudong1. A new blind deconvolution method based on signal subspace[J]. Journal of Vibration and Shock, 2022, 41(3): 139-147

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