摘要
针对滚动轴承故障初期故障信号微弱,故障特征难以识别问题,提出了一种基于多算法融合的滚动轴承早期微弱故障诊断的方法。首先,利用多小波自适应阈值法对轴承故障信号进行降噪处理;其次,利用快速谱峭度对消噪后的信号提取谱峭度最大的轴承冲击信号;接着,对剩余信号进行集合经验模态分解(EEMD)并基于峭度准则筛选前3个峭度最大的本征模态分量(IMF)同轴承冲击信号进行信号叠加,得到重构信号。最后,对重构信号进行快速谱相关,增强周期性成分,实现轴承早期微弱故障诊断。仿真实验表明:多算法融合的滚动轴承早期微弱故障诊断的方法可有效消弱噪声成分的干扰,增强微弱故障特征,实现滚动轴承早期微弱故障诊断。
To solve the problems of weak fault signals in the early stages of rolling bearing faults and difficulty in identifying fault characteristics,a method for early weak fault diagnosis of rolling bearings based on multi-algorithm fusion is proposed.First,the multi-wavelet adaptive threshold method is used to denoise the bearing fault signal.Second,the fast spectral kurtosis is used to extract the bearing impact signal with the largest spectral kurtosis from the denoised signal.Then,an ensemble empirical mode decomposition(EEMD)dissolved the residual signal.The first three eigenmodal components(IMF)with the largest kurtosis are selected based on the kurtosis criterion and superimposed with the bearing impact signal to obtain the reconstructed signal.Finally,fast spectral correlation is performed on the reconstructed signal to enhance the periodic component and realize early weak fault diagnosis of the bearing.The simulation experiments show that the multi-algorithm fusion method for early weak fault diagnosis of rolling bearings can effectively reduce the interference of noise components,enhance weak fault characteristics,and realize early weak fault diagnosis of rolling bearings.
作者
秦阳
卢岩(指导)
QIN Yang;LU Yan(School of Electrical Engineering,Shanghai Dianji University,Shanghai 201306,China)
出处
《上海电机学院学报》
2024年第2期77-82,共6页
Journal of Shanghai Dianji University
关键词
滚动轴承
多小波
快速谱峭度
快速谱相关
特征提取
rolling bearing
multi-wavelet
fast spectral kurtosis
fast spectral correlation
feature extraction
