基于小波降噪与分形理论的滚动轴承故障诊断  

Fault Diagnosis of Rolling Bearings Based on Wavelet De-noising and Fractal Theory

作　　者：卓蒙蒙[1] 张晓光[1] 姬程鹏[1] 雷大江[2] 

机构地区：[1]中国矿业大学机电工程学院,江苏徐州221008 [2]大连交通大学管理学院,辽宁大连116052

出　　处：《轴承》2011年第6期35-37,41,共4页Bearing

摘　　要：为了识别滚动轴承振动信号中包含的故障类型,运用小波对采集到的信号进行降噪,通过计算降噪后振动信号的关联维数,判断信号中是否包含故障。并以关联维数为特征量,通过计算各工况之间的距离函数,判断属于何种故障的信号。结果表明,运用分形理论进行故障诊断具有很强的实用价值。In order to recognize the fault type in rolling bearing vibration signals,the wavelet is applied to decrease the noise of collected signal and the correlation dimension of de-noise vibration signal is calculated to judge if fault is involved in signal.Moreover,the correlation dimension is employed as characteristic value and the type of fault signal is judged by calculating distance functions between various operating conditions.The results show that using fractal theory on fault diagnosis has great practical value.

关 键 词：滚动轴承 故障诊断 小波降噪 分形理论 关联维数 距离函数 

分 类 号：TH133.33[机械工程—机械制造及自动化] TP391.77[自动化与计算机技术—计算机应用技术]