经验模态分解与阶次跟踪分析对机器人用精密减速器故障诊断

Empirical Mode Decomposition and Order Tracking Analysis of Fault Diagnosis of Robot with Precision Reducer

针对行星摆线针轮减速器的故障特征在摆动疲劳实验中难以提取的问题,本文提出了一种经验模态分解(Empirical mode decomposition,EMD)与阶次跟踪分析结合的方法对疲劳实验中的减速器进行故障诊断。通过对采集到的时域信号进行等角域重采样、经验模态分解、计算固有模态分量(Intrinsic mode function,IMF)的峭度值、选取固有模态分量重构、快速傅里叶变换(Fast fourier transform,FFT)后得到阶次图进行验证。结果表明:该方法能够准确的提取包含故障信息的固有模态分量,实现了行星摆线针轮减速器在非平稳状态下的故障特征识别。

Because the fault features of a planetary cycloid pinwheel reducer are difficult to extract in the swing fatigue experiment,this paper proposes a method that combines empirical mode decomposition and order tracking analysis to conduct the fault diagnosis of the reducer.Through performing equiangular domain resampling,empirical mode decomposition,calculating the kurtosis value of the intrinsic mode function,selecting the intrinsic mode component for reconstruction and the fast Fourier transform of collected time domain signals,the order map is verified.The results show that the method can accurately extract the inherent modal components that contain the information on faults and identify the fault features of the planetary cycloid pinwheel reducer in the non-stationary state.