摘要
针对转子运转时的振动冲击和噪声较大从而容易掩盖振动信号中的故障特征的问题,提出了一种基于小波阈值去噪的EEMD故障特征识别方法。采用改进后小波阈值滤波方法对振动信号进行降噪预处理,对处理结果进行集合经验模态分解(EEMD),再依据峭度原则筛选分解得到的本征模态函数(IMF)。分析重构信号的频谱特征以识别故障。结果表明,该方法有效提高了信噪比且能提取到转子故障特征。
An ensemble empirical mode decomposition(EEMD)fault feature identification method based on wavelet threshold denoising has been proposed aiming at the problems of vibration impact and noise when the rotor is running,which is easy to mask the fault features in the vibration signal.The wavelet threshold denoising method was used to preprocess the vibration signal,and then the intrinsic modal function(IMF)obtained after the set of EEMD was selected according to the Kurtosis principle.The fault characteristics of the time-frequency analysis of the obtained results were analyzed.The results show that this method can effectively suppress the noise and extract the characteristics of rotor fault.
作者
吕世鹏
袁亮
冉祥锋
LV Shipeng;YUAN Liang;RAN Xiangfeng(School of Mechanical Engineering,Xinjiang University,Urumqi Xinjiang 830047,China)
出处
《机床与液压》
北大核心
2019年第13期192-195,228,共5页
Machine Tool & Hydraulics
基金
国家自然科学基金资助项目(61662075)
新疆自治区科技支疆项目(2017E0284)
乌鲁木齐科技人才计划(P151010006)
关键词
小波阈值去噪
EEMD
峭度
故障诊断
转子
Wavelet threshold denoising
EEMD
Kurtosis
Fault diagnosis
Rotor
