摘要
针对非线性、强背景噪声下滚动轴承振动信号早期故障特征微弱,难以识别的问题,提出一种改进经验小波变换(EWT)降噪和快速谱相关相结合的滚动轴承早期微弱故障诊断方法。针对EWT频带划分方式受噪声影响较大,存在划分不合理的问题,提出极大值包络处理的划分方式;采用改进的EWT进行自适应信号分解,获得不同的固有模态分量,采用峭度准则筛选出有用模态分量,并进行重构得到降噪后的信号;为增强早期故障信号中的故障冲击周期成分,对降噪后的信号采用快速谱相关(Fast-SC)进行分析,获得平方增强包络谱;对平方包络谱中幅值突出的成分与故障频率进行对比分析,实现早期故障诊断。结果表明:与快速谱分析、改进EWT降噪结合快速谱峭度图相比,所提方法能有效增强早期故障特征频率,实现早期故障的准确诊断。
Aiming at the problem that the early fault characteristics of rolling bearing vibration signals with nonlinear and strong background noise are weak and difficult to identify,a rolling bearing early weak fault diagnosis method combining improved empirical wavelet transform(EWT)noise reduction with fast spectral correlation was proposed.In view of the problem that the EWT frequency band division method was greatly affected by noise and the division was unreasonable,the division method of maximum envelope processing was proposed;the improved EWT was used to perform adaptive decomposition of the signal to obtain intrinsic mode functions,and the kurtosis criterion was used to reconstruct each IMF to obtain the denoised signal;to enhance the period component of the early fault signal,the noise-reduced signal was analyzed by using fast spectral correlation,and the square enhanced envelope spectrum was obtained;the components with prominent amplitude in the squared envelope spectrum and the fault frequency were compared and analyzed to realize early fault diagnosis.The results show that compared with the fast spectrum analysis,the improved EWT noise reduction combined with the fast spectrum kurtosis diagram,by using the proposed method,the early fault characteristic frequency can be effectively enhanced,and the accurate diagnosis of the early fault can be realized.
作者
朱朋
裴雪武
周祖清
ZHU Peng;PEI Xuewu;ZHOU Zuqing(School of Mechantronics and Vehicle Engineering,Chongqing Jiaotong University,Chongqing 400074,China)
出处
《机床与液压》
北大核心
2022年第18期158-164,共7页
Machine Tool & Hydraulics
基金
重庆交通大学研究生教育创新基金项目(2021S0036)。
关键词
经验小波变换
快速谱相关
滚动轴承
早期故障诊断
Empirical wavelet transform
Fast spectral correlation
Rolling bearing
Early fault diagnosis
