摘要
滚动轴承早期故障信号中故障信息比较微弱常常被强噪声所掩盖,增加了对滚动轴承故障诊断的难度。针对这一问题,笔者提出了基于自适应最优Morlet小波变换的滚动轴承故障诊断方法。首先,利用粒子群优化算法对Morlet小波变换的核心参数进行自适应寻优,在获得最优Morlet小波的同时保证了良好的带通滤波性能;然后,将最优Morlet小波对滚动轴承早期故障信号进行滤波去噪,提高信号的信噪比;最后,对最优Morlet小波滤波信号进行包络谱分析,通过包络谱中的主导频率成分与滚动轴承各元件的故障特征频率对比从而判断轴承的故障位置。仿真数据和实测数据分析结果证明,笔者所提方法能够有效提取故障信号中的特征信息,具有一定的有效性。
The early stage weak impulsive fautt feature is so weak that it is always covered by environmental noise,which increases the fautt diagnosis difficulty of rolling bearing.Aiming to this problem,a new diagnosis method based on adaptive optimal Morlet wavelet transform is proposed.Firstly,The core pa rameter of Morlet wavelet transform is calculated by particle swarm optimization(PSO)adaptively,which guarantees optimal Morlet based wavelet as well as wonderful band-pass filter performance;Secondly,in order to improve signal-to-noise ratio,optimal Morlet wavelet is used to filter incipient fautt signal of rolling bearing;Finally,optimal Morlet wavelet filtered signal is analyzed by envelope spectrum,and the fautt location of rolling bearing is extracted by contrasting the major frequency with the fautt frequency of rolling bearing.The analysis results of simulated signal and measured signal show that the proposed method is able to extract the fault impulse signal.
作者
祝小彦
王永杰
张钰淇
袁婧怡
ZHU Xiaoyan;WANG Yongjie;ZHANG Yuqi;YUAN Jingyi(School of Energy Power and Mechanical Engineering,North China Electric Power University Baoding,071003,China)
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2018年第5期1021-1029,1085,共10页
Journal of Vibration,Measurement & Diagnosis
基金
河北省自然科学基金资助项目(E2018502059)
关键词
MORLET小波
滚动轴承
早期故障诊断
特征提取
Morlet wavelet transform
rolling bearing
incipient fault diagnosis
feature extraction
