中国空间科学技术

中国空间科学技术 ›› 2020, Vol. 40 ›› Issue (3): 100-106.doi: 10.16708/j.cnki.1000-758X.2020.0037

• 技术交流 • 上一篇    下一篇

一种基于模糊聚类模型的动量轮健康性排序方法

季业1,2，崔振1,2,*，王雪涛1,2，严嵘1,2，刘一帆3   

  1. 1. 北京控制工程研究所, 北京 100094
    2. 空间智能控制技术重点实验室，北京 100094
    3. 北京空间飞行器总体设计部，北京 100094
  • 收稿日期:2019-12-17 修回日期:2020-02-09 录用日期:2020-02-25 发布日期:2020-05-29 出版日期:2020-06-25
  • 通讯作者: 崔振（1977-），男，高级工程师，研究方向为卫星姿态控制技术、卫星自主健康管理技术，5277981@qq.com。
  • 作者简介:季业（1983-），男，硕士，研究方向为人工智能与故障诊断技术、 小批量卫星风险与故障管理技术，18610350632@163.com。

A momentum wheel health ranking method based on fuzzy clustering model

JI Ye1,2, CUI Zhen1,2,*, WANG Xuetao1,2, YAN Rong1,2, LIU Yifan3   

  1. 1. Beijing Institute of Control Engineering，Beijing 100094，China
    2. Science and Technology on Space Intelligent Control Laboratory，Beijing 100094，China
    3. Beijing Institute of Spacecraft System Engineering，Beijing 100094，China
  • Received:2019-12-17 Revision received:2020-02-09 Accepted:2020-02-25 Online:2020-05-29 Published:2020-06-25

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摘要： 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。

关键词: 模糊, 聚类, 动量轮, 健康性, 排序

Abstract: 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.

Key words: fuzzy；clustering, momentum wheel, health, ranking