基于小波时频框架分解方法的滚动轴承故障诊断

FAULT DIAGNOSIS OF ROLLING ELEMENT BEARINGS BASED ON WAVELET TIME-FREQUENCY FRAME DECOMPOSITION

损伤点通过其它元件时引起的周期性冲击是判断滚动轴承局部损伤故障的关键特征信息。针对滚动轴承的振动特点,设计了小波时频框架,利用框架分解方法在匹配信号特征结构,直接提取特征信息方面的优势,分析了滚动轴承的振动信号。根据框架分解结果,在时频联合域内清晰直观地提取了滚动轴承局部损伤故障的周期性冲击特征,识别了滚动体、内圈和外圈的单点缺陷,与小波变换的对比验证了框架分解在检测滚动轴承局部损伤故障方面的有效性。

Periodic impulses characterize the vibration of rolling element bearings with localized defect Wavelet time-frequency.frame decomposition can best match the characteristic structures of a signal globally,and extract the time-frequency information directly.It is employed to analyze the vibration signals of both normal and faulty rolling element bearings with single point defect in rolling element,inner race and outer race.In order to extract the transient features of rolling element bearing vibration,especially the periodic impulses,wavelet time-frequency frames are designed.By means of the frame decomposition,the periodic impulses are extracted in joint time-frequency domain,and the single point defects are identified accordingly.From the comparison with continuous wavelet transformation based on basis expansion,it is found that the frame decomposition is effective in simultaneously extracting the impulses,harmonics and other transient phenomena of rolling element bearing vibration signals,and is feasible in identifying the localized defects of rolling element bearings.