摘要
针对强背景噪声下的齿轮微弱故障特征提取问题,提出了一种将级联单稳随机共振与经验模式分解(EMD)-Teager能量算子解调方法相结合的特征提取方法。首先对含噪故障信号进行随机共振输出,降噪后再进行经验模式分解,分解得到具有不同特征时间尺度的固有模态函数(IMFs),最后通过Teager能量算子解调方法求取每个有效IMF分量的幅频信息,从而提取齿轮微弱故障特征。仿真分析和实际测试结果均表明,通过随机共振降噪后,该方法能有效检测出齿轮局部损伤故障特征频率。
Aimed at the feature extraction problem of weak gear faults under strong background noise, an early feature extraction method was proposed based on cascaed monostable stochastic reso- nance(CMSR) system and EMD with Teager energy operator demodulating. Firstly CMSRS was em- ployed as the preprocessing to remove noise, and then the denoised signals were decomposed into a se- ries of intrinsic mode functions(IMFs) of different scales by EMD. Finally, Teager energy operator demodulating was applied to get amplitudes and frequencies of each effective IMF so as to extract the faint gear fault features. The simulation and application results show that the proposed method can detect the characteristic frequency of gear faults of local damage effectively after the noise reduction by CMSR.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2014年第4期539-546,共8页
China Mechanical Engineering
基金
国家自然科学基金资助项目(10972207
60908039)
浙江省公益性应用研究计划资助项目(2013C31098)
关键词
级联
单稳随机共振
经验模式分解
TEAGER能量算子
cascaded
monostable stochastic resonance
empirical mode decomposition (EMD)
Teager energy operator