旋转机械故障特征提取的全矢二元经验模态分解方法研究

黄传金 1,2,雷文平3,李凌均3,孟雅俊1,赵静1

振动与冲击 ›› 2019, Vol. 38 ›› Issue (9) : 94-99.

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振动与冲击 ›› 2019, Vol. 38 ›› Issue (9) : 94-99.
论文

旋转机械故障特征提取的全矢二元经验模态分解方法研究

  • 黄传金 1,2,雷文平3,李凌均3,孟雅俊1,赵静1
作者信息 +

Full vector BEMD method for fault feature extraction of rotating machinery

  • HUANG Chuanjin1,2,LEI Wenping3, LI Lingjun3, MENG Yajun1, ZHAO Jing1
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摘要

为更准确提取旋转机械故障特征,提出了基于全矢二元经验模态分解(Bivariate empirical mode decomposition,BEMD)的故障特征提取方法。该方法首先通过多传感器正交采集旋转机械故障同一截面上的振动信号,并将其组成一个复数;然后运用BEMD将复数按旋转速度从高到低的顺序自适应地分解到各自的频带,得到系列复固有模态分量(Complex Intrinsic Mode Functions, CIMFs);提出复数相关系数的概念,并用于组合CIMFs得到新的复旋转分量以防同一频率的信号被分解到不同的CIMFs;最后,运用全矢谱融合组合后的CIMFs的特征信息,得到幅频、角度和进动方向等信息。与全频谱方法的对比实验结果表明本文所提方法的有效性。

Abstract

A fault feature extraction method based on full vector bivariate empirical mode decomposition (BEMD) is proposed for extracting the fault characteristics of rotating machinery more accurately. Firstly, the vibration signals on the same cross section of rotating machinery faults are collected by orthogonal multi sensors, and a complex signal is formed. Then, the BEMD algorithm is applied to decompose adaptively complex signal into the different frequency bands signals according to the rotation speed, which are called complex intrinsic mode functions (CIMFs). Finally, the full vector spectrum technique is used to fuse the characteristic information of CIMFs, and the amplitude frequency, angle and precession direction are obtained. The effectiveness of the proposed method is demonstrated by the comparison with the full spectrum.
 

关键词

旋转机械 / 特征提取 / 二元经验模态分解 / 全矢谱

Key words

rotating machinery / fault feature / bivariate empirical mode decomposition / full vector spectrum

引用本文

导出引用
黄传金 1,2,雷文平3,李凌均3,孟雅俊1,赵静1. 旋转机械故障特征提取的全矢二元经验模态分解方法研究[J]. 振动与冲击, 2019, 38(9): 94-99
HUANG Chuanjin1,2,LEI Wenping3, LI Lingjun3, MENG Yajun1, ZHAO Jing1. Full vector BEMD method for fault feature extraction of rotating machinery[J]. Journal of Vibration and Shock, 2019, 38(9): 94-99

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