基于小波包-AR谱的变速器轴承故障特征提取  

Automobile Transmission Bearing Fault Feature Extraction Based on Wavelet Packet-Auto Regressive Model Spectrum

作　　者：张玲玲[1,2] 赵懿冠[2] 肖云魁[2] 骆诗定[2] 廖红云[2] 潘全先[2] 

机构地区：[1]军械工程学院火炮工程系,石家庄050003 [2]军事交通学院汽车工程系,天津300161

出　　处：《振动．测试与诊断》2011年第4期492-495,537,共4页Journal of Vibration,Measurement & Diagnosis

基　　金：总装备部预研项目(编号:40407030302)

摘　　要：提出了一种小波包-AR谱估计和计算散度相结合的汽车变速器轴承故障特征提取方法。将6种不同磨损状况下的变速器轴承振动信号进行小波包分解,重构各频段信号并进行自回归(auto regressive,简称AR)谱估计,最后计算各故障轴承到新轴承之间的散度值。试验结果表明,不论是轴承的轴向间隙,还是径向间隙差异及疲劳剥落,在小波包-AR谱的谱图上均有明显的反映,该方法可以有效提取出汽车变速器轴承振动信号中的故障特征。A novel method of fault feature extraction for automotive transmission bearing based on combination of wavelet packet-auto regressive model spectrum and divergence calculation is presented.The vibration signals under six different wear conditions are decomposed with wavelet packet,the time series at each frequency band are reconstructed,and reconstructed signals are analyzed by AR spectrum estimation.At last,divergence is calculated between new bearing and the other five ones.The experimental result indicates that transmission bearing fault features,such as axial clearance,radial clearance or fatigue spalling,can be shown in wavelet packet-AR Spectrum evidently.This method is efficient in automobile transmission bearing fault feature extraction.

关 键 词：小波包 AR谱 变速器轴承 故障特征提取 

分 类 号：U463.2[机械工程—车辆工程] TN911.7[交通运输工程—载运工具运用工程]