摘要
针对在强噪声背景下轴承早期微弱故障特征难以提取问题。提出了一种基于同步压缩小波变换(Synchrosqueezing Wavelet Transform,SWT)和最大相关峭度反卷积(Maximum correlation kurtosis deconvolution,MCKD)相结合的滚动轴承故障诊断方法。首先采用SWT方法将多分量振动信号分解为若干内禀模态分量(IMT),然后选取合适IMT重构信号,对重构信号进行MCKD滤波,突出故障冲击成分,最后对滤波后信号包络解调,提取故障特征,实现滚动轴承的更准确诊断。
It is difficult to extract the early weak fault characteristics of bearing under the background of strong noise.This paper presents a rolling bearing fault diagnosis method based on the synchronous compression wavelet transform(SWT)in combination with maximum correlation kurtosis deconvolution(MCKD).Firstly,the multi-component vibration signal is decomposed into several intrinsic mode type(IMT)components by SWT method.Then the appropriate IMT reconstructed signal is selected to perform MCKD filtering on the reconstructed signal to highlight the fault impact components.Finally,the filtered signal is envelope demodulated to extract the fault characteristics,and the accurate diagnosis of rolling bearing is realized.
作者
张旭光
荆双喜
杨白冰
冷军发
罗晨旭
ZHANG Xuguang;JING Shuangxi;YANG Baibing;LENG Junfa;LUO Chenxu(Sohool of Mechanical and Power Engineering,Henan Polytechnic University,Jiaozuo Henan 454000,China)
出处
《机械设计与研究》
CSCD
北大核心
2021年第2期73-77,共5页
Machine Design And Research
基金
国家自然科学基金资助项目(U1804134)
河南省科技攻关项目(172102210021,202102210070)
河南省高等学校重点科研项目(19A440007)。
关键词
故障诊断
滚动轴承
同步压缩小波变换
最大相关峭度反卷积
fault diagnosis
rolling bearing
synchronous compression wavelet transform
maximum correlation kurtosis deconvolution
