一种谐波减速器不对中误差分析方法

在空间环境中运行的星载天线转动单元中的谐波减速器产生的不对中故障信号故障特征难以提取，针对此问题提出了一种优化变分模态分解的故障诊断方法，这种方法基于鲸鱼优化算法(WOA)和Teager能量算子(TEO)包络解调。首先，由于包络熵对故障信号特征非常敏感，故采用包络熵作为适应度函数，同时其也是鲸鱼优化算法的目标函数。其次，WOA具有操作简单、调整参数少、跳出局部最优能力强等诸多优点，故使用WOA对变分模态分解(VMD)进行优化，构建优化算法。最后，使用Teager能量算子对本征模态分量(IMFC)进行处理。所提出的方法用于分析谐波减速器输入、输出轴两端收集的实验信号。通过与传统的快速傅里叶变换方法进行对比结果表明，所提出的方法提高了分解效率，在分解参数的原则和故障信号提取精度方面具有良好的性能，误差不超过2%,能够实现谐波减速器不对中故障信号的精确诊断，具有一定的研究意义与工程实用价值。

A method of misalignment error of space antenna harmonic reducer

The harmonic reducer used in aerospace requires high transmission accuracy，and the error caused by the misalignment problem of the reducer cannot be ignored.Therefore，the fault feature extraction method of the misalignment problem of the harmonic reducer has become the focus of the research.A new method termed optimal variational mode decomposition（VMD）was proposed to extract harmonic reducer misalignment fault features.Firstly，since envelope entropy is very sensitive to fault signal features，it is used as a fitness function，which is an objective function for the whale optimization algorithm（WOA）.Secondly，WOA has numerous merits such as simple operation，fewer adjustment parameters and a strong ability for jumping out of local optimum，and is applied to the optimization of VMD.Finally，intrinsic mode function components were processed through a Teager energy operator.The suggested approach was employed to analyze the experimental signal collected from harmonic reducer.The comparison results show that the advanced method is more effective and demonstrates superiority over empirical mode decomposition，local mean decomposition and wavelet packet decomposition.