摘要
滚动轴承是多数工程机械的关键零部件,在长时间工作后,其内、外圈常常出现各种程度的疲劳裂纹,影响机器的正常运行。为了提取由故障产生的非平稳信号冲击特征,本文基于小波变换,对原始信号进行多层次分解,通过限定阈值的方法,有效地从振动信号中剥离高强度背景噪声,强化故障特征表达,为轴承故障诊断提供更为有效的数据信号。
Rolling bearings are the key components of most construction machinery.After working for a long time,various degrees of fatigue cracks often appear in the inner and outer rings,which affects the normal operation of the machine.To extract the non-stationary signal impact characteristics caused by the fault,this paper based on wavelet transform,decomposed the original signal with multiple levels.Through the method of limiting the threshold,the high-intensity background noise from the vibration signal was effectively stripped,and the fault feature expression was strengthened.This paper provides more effective data signals for the bearing fault diagnosis.
作者
石泽
Shi Ze(Chang'an University,School of Construction Machinery,Shaanxi Xi'an710064)
出处
《南方农机》
2020年第15期134-135,共2页
关键词
小波变换
滚动轴承
降噪滤波
wavelet transform
rolling bearing
noise reduction filter
作者简介
石泽(1996-),男,陕西渭南人,硕士研究生,研究方向:机械信号处理与故障诊断。
