基于曲线特征分段的多阶FRFT滤波提取齿轮早期故障特征

王国威1, 梅检民 2,曾锐利2,常春1

振动与冲击 ›› 2016, Vol. 35 ›› Issue (11) : 161-166.

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振动与冲击 ›› 2016, Vol. 35 ›› Issue (11) : 161-166.
论文

基于曲线特征分段的多阶FRFT滤波提取齿轮早期故障特征

  • 王国威1, 梅检民 2,曾锐利2,常春1
作者信息 +

Extraction of gearbox fault features based on segmenting with features of curve and multilevel fractional fourier transformation filtering

  • WANG Guo-wei1,MEI Jian-min2, ZENG Rui-li2,CHANG Chun1,
Author information +
文章历史 +

摘要

对于变速器齿轮早期微弱故障特征难以提取的问题,提出了一种基于曲线特征进行分段的多阶分数阶傅里叶变换(Fractional Fourier Transform,FRFT)滤波方法。首先,依据振动信号频率曲线的特征将目标档位的啮合频率曲线分成若干频率近似线性变化的信号段,然后通过计算确定相应信号段的分数阶傅里叶变换的滤波最佳阶次,再逐段进行滤波,从而分离出包含故障特征信息的目标阶比分量,进而进行故障特征提取。通过采用该方法分析变速器变加速工况振动信号,结果表明,基于曲线特征进行分段的多阶FRFT滤波能够有效分离出啮合频率分量,对分离出的分量进行阶次包络解调分析,能准确提取出齿轮微弱故障特征。

Abstract

To solve the difficulty of extracting early weak fault features of the transmission bearing, a method of multilevel fractional fourier transformation (FRFT) filtering based on segmenting with features of curve is proposed. Firstly, the time-frequency curve of a gearbox in target-gear was segmented according the features of curve,so the frequency in each segment was approximately linear. Then the best FRFT order of each segment was calculated and each segment was filtered to eliminate interference components,at last the fault features were extracted. The vibration signal was analyzed with FRFT, and the test results showed that the target order component under variably accelerated conditions can be extracted using multilevel FRFT; gear fault features can be obtained with envelope demodulation of the extracted sole order component. 
 
 

关键词

基于曲线特征分段 / 多阶FRFT滤波 / 变加速工况 / 特征提取

Key words

segmentation based on features of curve / multilevel FRFT filtering / variably accelerated conditions / feature extraction

引用本文

导出引用
王国威1, 梅检民 2,曾锐利2,常春1. 基于曲线特征分段的多阶FRFT滤波提取齿轮早期故障特征[J]. 振动与冲击, 2016, 35(11): 161-166
WANG Guo-wei1,MEI Jian-min2, ZENG Rui-li2,CHANG Chun1, . Extraction of gearbox fault features based on segmenting with features of curve and multilevel fractional fourier transformation filtering[J]. Journal of Vibration and Shock, 2016, 35(11): 161-166

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