摘要
为了准确提取滚动轴承早期微弱故障特征,提出基于自适应优化的可调品质因子小波变换(tunable Q-factor wavelet transform,TQWT)轴承早期故障诊断方法。该方法利用包络谱特征频率强度系数的参数自适应寻优方法来自适应优化TQWT,以弥补传统TQWT参数选择过分依赖人工经验的不足。首先利用该方法获得振动信号的最优分解结果,然后通过分析最优分解结果的包络谱来判断轴承故障类型。通过分析仿真信号以及工程试验数据证明了该方法的有效性。
In order to extract accurately the early weak fault characteristics of rolling bearings,a bearing early fault diagnosis method based on adaptive optimization tunable Q-factor wavelet transform(TQWT)is proposed.In this method,the TQWT method is optimized based on envelope spectrum characteristic frequency intensity coefficient to compensate for the shortage of traditional TQWT parameter selection,which relies too much on artificial experience.First,the optimal decomposition results of the vibration signal are obtained by this method,and then the type of the bearing fault is judged by the analysis of the envelope spectrum of the optimal decomposition results.The effectiveness of the method described in this paper is proved by the analysis of the simulation signal and the engineering test data.
作者
黄慧杰
刘桐桐
任学平
HUANG Huijie;LIU Tongtong;REN Xueping(Institute of Mechanical Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,CHN)
出处
《制造技术与机床》
北大核心
2019年第2期137-142,共6页
Manufacturing Technology & Machine Tool
基金
内蒙古自治区高等学校科学研究项目(NJZY16154)
内蒙古科技大学创新基金项目(2015QDL10)
关键词
滚动轴承早期故障
可调品质因子小波变换
特征频率强度系数
early fault of rolling bearing
tunable Q-factor wavelet transform
characteristic frequency strength coefficient
