上海交通大学学报 ›› 2021, Vol. 55 ›› Issue (6): 716-728.doi: 10.16183/j.cnki.jsjtu.2019.254

所属专题: 《上海交通大学学报》2021年“航空航天科学技术”专题 《上海交通大学学报》2021年12期专题汇总专辑

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一种合成残差式的反作用轮故障检测方法

何夏维1,2, 蔡云泽1(), 严玲玲2   

  1. 1.上海交通大学 电子信息与电气工程学院, 上海 200240
    2.中国科学院微小卫星创新研究院, 上海 201203
  • 收稿日期:2020-09-05 出版日期:2021-06-28 发布日期:2021-06-30
  • 通讯作者: 蔡云泽 E-mail:yzcai@sjtu.edu.cn
  • 作者简介:何夏维(1991-),男,山东省临朐县人,工程师,现主要从事卫星姿态与轨道控制研究
  • 基金资助:
    国家自然科学基金重大科研仪器研制项目(61627810)

A Combined Residual Detection Method of Reaction Wheel for Fault Detection

HE Xiawei1,2, CAI Yunze1(), YAN Lingling2   

  1. 1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    2. Innovation Academy for Microsatellite, Chinese Academy of Sciences, Shanghai 201203, China
  • Received:2020-09-05 Online:2021-06-28 Published:2021-06-30
  • Contact: CAI Yunze E-mail:yzcai@sjtu.edu.cn

摘要:

为了在地面根据卫星遥测数据有效把握在轨卫星反作用轮的健康状态,设计了一种合成残差式的反作用轮故障检测方法.该方法针对在轨遥测数据的特点,根据实际可获得的闭环控制反作用轮遥测数据,利用极端梯度提升(XGBoost)回归模型进行转速预测生成残差,并结合黏性摩擦因数对摩擦力矩突变的敏感性进行故障检测.此外,还针对在轨遥测数据不完备的情况,对检测方法进行了验证.结果表明,该方法对于常见的在轨卫星反作用轮故障均有较好的检测效果.合成残差式故障检测方法不依赖于故障样本,对于数据样本的要求也较低,因此在实际地面故障检测系统中具有一定的应用价值.

关键词: 闭环系统, 反作用轮, 故障检测, 不完备数据, 机器学习

Abstract:

A fault detection method of combined residual is proposed to effectively master the health state of the reaction wheels of in-orbit satellite according to the telemetry data. Based on the characteristics of in-orbit telemetry data, in the proposed method, the XGBoost regression model is used to predict the rotation speed and generate residual with available data of the closed-loop controlled reaction wheel. The sensitivity of viscous friction coefficient to the sudden change of friction torque is also combined with the method. In addition, the fault detection method is verified in view of the incomplete telemetry data in orbit. The result shows that the proposed method has an excellent detection effect on common reaction wheel faults. The combined residual fault detection method does not rely on fault samples and has low requirements for samples, so it has a certain application value in practical fault detection system.

Key words: closed-loop system, reaction wheel, fault detection, incomplete data, machine learning

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