连续小波变换在机械故障特征提取中的应用  

Application of CWT in Mechanical Fault Feature Extraction

作　　者：张澎涛[1] 刘晋浩[2] 

机构地区：[1]东北林业大学机电工程学院,哈尔滨150040 [2]北京林业大学工学院,北京100083

出　　处：《吉林大学学报（信息科学版）》2014年第2期172-176,共5页Journal of Jilin University（Information Science Edition）

基　　金：引进国际先进林业科学技术"948"基金资助项目(2013-4-20)

摘　　要：为解决提取齿轮故障特征时去除外部噪声的问题,以连续小波变换和自相关系数法为理论依据,以缺齿齿轮故障为例,提出了一种齿轮故障诊断方法。该方法能从所测量的含噪信号中确定出故障脉冲所对应的时间节点。利用多通带滤波器进行滤波处理,可以从提取的故障特征中有效地剔除寄生脉冲。实验表明,该方法能准确识别断齿振动信号的故障特征。In order to solve the problem that can not denoising the external noise when extracting fault feature of gear,this paper introduces a method that can identify the time of periodic impulsive fault signatures from the measured noisy signal mixture on the basis of CWT （Continuous Wavelet Transfon） and auto-correlation coefficient method.A comb filter can be applied to extract fault features in time-scale domain,the spurious impulses can be removed effectively from the extracted fault feature.Experiments show that this method can accurately identifiy the fault feature of impulsive signals with missing tooth.

关 键 词：连续小波变换 自相关系数 齿轮 故障诊断 特征提取 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]