摘要
采用总体经验模态分解与混合高斯隐马尔可夫模型相结合方法对齿轮故障进行诊断。首先采用仿真实验验证了总体经验模态分解在消除模态混叠方面的有效性;其次,提出了基于总体经验模态分解-混合高斯隐马尔可夫模型的齿轮故障诊断框架;进而将所提方法应用到齿轮箱故障诊断实验中;最终,实验结果验证了该方法的有效性。
A new method is proposed based on EEMD and Mixture Gaussian hidden Markov mode （MGHMM） for gear fault diagnosis. Firstly, a simulation signal is used to verify the advantages of EEMD comparing to EMD in eliminating the mode mixing; secondly, the framework of gear diagnosis based on EEMD and MGHMM is built; thirdly, the new method is applied to the gear fault diagnosis. The results show that the method can identify gear fault accurately and effectively.
出处
《机械传动》
CSCD
北大核心
2012年第11期27-31,35,共6页
Journal of Mechanical Transmission
