摘要
针对滚动轴承故障信息微弱且常受到强背景噪声影响,导致故障特征提取困难的问题,提出一种改进变分模态分解(VMD)和Teager能量算子的滚动轴承弱故障特征提取方法。该方法以最大加权频域相关峭度为目标函数,对影响VMD分解的参数进行优化选取,确保获取故障特征最为明显子信号。利用Teager能量算子处理获取的最优子信号,增强故障冲击特征,并借助快速傅里叶变换准确提取出滚动轴承故障特征。将该方法运用到滚动轴承仿真和实验信号中,结果表明,提出的方法能够解决VMD参数难以选取的问题,且能从强背景噪声干扰中有效提取出滚动轴承内圈弱故障特征,实现了故障的准确识别,具有一定的实际应用价值。
As the fault information of rolling element bearing is weak and often affected by strong background noise,the fault feature extraction may be very difficult.Hence,a fault feature extraction approach was proposed based on the combination of improved variational mode decomposition(VMD)and Teager energy operator.The method took the maximum weighted correlated kurtosis in frequency domain as the objective function,the parameters in the VMD was optimized to ensure that the subsignal with obvious fault features was obtained.The Teager energy operator was employed to further enhance the fault features and the fast Fourier transform was used to acquire the Teager energy spectrum where the fault information could be easily detected.The results of simulation verification and experimental verification shows that the proposed method can select the parameters of VMD properly,and the fault feature of rolling element bearing can be extracted effectively from the strong background noise.Therefore,the proposed approach has certain practical application value.
作者
朱群伟
朱丹宸
张明悦
Zhu Qunwei;Zhu Danchen;Zhang Mingyue(Guangzhou Military Representative Office in Zhanjiang Area,Representation Bureau of Naval Equipment Department,Zhanjiang,Guangdong 524005,China;Naval Petty Officer Academy,Bengbu,Anhui 233012,China;92601 PLA Troops,Zhanjiang,Guangdong 524009,China)
出处
《机电工程技术》
2021年第6期270-275,共6页
Mechanical & Electrical Engineering Technology
关键词
变分模态分解
TEAGER能量算子
滚动轴承
故障特征提取
variational mode decomposition
Teager energy operator
rolling element bearing
fault feature extraction
