一种基于模糊聚类模型的动量轮健康性排序方法

基于模糊聚类分析提出一种动量轮健康性排序方法，选取实际型号任务中50N·m·s动量轮的地面测试数据与在轨数据，对数据处理后，研究闭环工况动量轮动态转速的电机电流，利用相关系数法将其转换为模糊相似矩阵，聚类分析动量轮健康性最大阈值；同时以最大阈值为标准，设计判断准则，用4个动量轮的数据验证该方法的实用性并与峭度试验方法进行对比。结果表明：该方法可以准确对动量轮健康性进行排序。

A momentum wheel health ranking method based on fuzzyclustering model

Based on the fuzzy clustering analysis, a momentum wheel health ranking method was proposed. The ground test data and on-orbit data of 50N·m·s momentum wheel in actual model tasks were selected. After the data were processed, the instantaneous dynamic speeds of the momentum wheels in closed loop conditions were studied, then the data were converted into the fuzzy similarity matrix by the correlation coefficient method, and cluster analysis was conducted for the maximum threshold of risks. Meanwhile, the maximum threshold was used as a criterion to design the judgment criteria. The data of the four momentum wheels were used to verify the practicability of the method and compared with the kurtosis test method. The results show that this method can accurately rank the health of momentum wheels.