基于深度森林的卫星ACS执行机构与传感器故障识别

doi:10.7527/S1000-6893.2019.23778

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基于深度森林的卫星ACS执行机构与传感器故障识别

程月华¹, 江文建¹, 杨浩¹, 薛琪¹, 廖鹤²

1. 南京航空航天大学自动化学院, 南京 211100;
2. 南京航空航天大学航天学院, 南京 210016

收稿日期:2019-12-13 修回日期:2019-12-26 出版日期:2020-06-30 发布日期:2020-01-02
通讯作者: 程月华 E-mail:chengyuehua@nuaa.edu.cn
基金资助:
国家重点研发计划（2016YFB0500803）；装备预研国防科技重点实验室基金（1422080307）

Fault identification of actuators and sensors of satellite attitude control systems based on deep forest algorithm

CHENG Yuehua¹, JIANG Wenjian¹, YANG Hao¹, XUE Qi¹, LIAO He²

1. School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China;
2. School of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received:2019-12-13 Revised:2019-12-26 Online:2020-06-30 Published:2020-01-02
Supported by:
National Key R&D Program of China (2016YFB0500803); Equipment Pre-research National Defense Science Technology Key Laboratory Foundation under Grant (1422080307)

摘要/Abstract

摘要： 针对卫星姿态控制系统（ACS）闭环回路的故障难以辨识的问题，引入深度森林算法，实现执行机构与传感器故障识别。首先针对可获取的少量卫星姿态控制系统遥测数据，结合系统动力学特性，研究合适的特征选择和特征提取方法，再结合深度森林算法进行故障信息学习与辨识，建立故障预测模型，实现执行机构故障与传感器故障的识别。半物理仿真结果表明：在存在气浮台干扰力矩、卫星转动惯量未知、飞轮非线性特性、闭环故障传播等多种不利因素情况下，深度森林算法对于执行机构和传感器故障具有高效的识别能力。

关键词: 深度森林算法, 卫星姿态控制系统, 执行机构, 传感器, 故障识别

Abstract: The difficulty in identifying the faulted sensors and actuators of satellite Attitude Control Systems (ACS) resides in the spreading of faults in the closed loop. The deep forest algorithm is introduced in this study to build a fault prediction model to achieve the isolation of the sensor faults and actuator faults. After collecting healthy ACS telemetry data to group a training set according to the dynamic characteristics of ACS, we apply appropriate feature selection and extraction methods to the training set, obtaining the features of both the sensor faults and actuator faults. The deep forest algorithm, with its strong generalization ability, is then used to learn and identify fault information, thereby establishing a fault prediction model to realize the recognition of actuator and sensor faults. The results of the semi-physical simulation indicate that the proposed method can identify the faults of sensors and actuators effectively in the presence of many uncertain factors, such as the interference moments of air bearing testbeds, unknown moments of the inertia of satellites, nonlinear characteristics of flywheels and fault propagation in the closed loop of ACS.

Key words: deep forest algorithm, satellite attitude control systems, actuators, sensors, fault identification

中图分类号:

V474

程月华, 江文建, 杨浩, 薛琪, 廖鹤. 基于深度森林的卫星ACS执行机构与传感器故障识别[J]. 航空学报, 2020, 41(S1): 723778-723778.

CHENG Yuehua, JIANG Wenjian, YANG Hao, XUE Qi, LIAO He. Fault identification of actuators and sensors of satellite attitude control systems based on deep forest algorithm[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(S1): 723778-723778.

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E-mail：hkxb@buaa.edu.cn

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基于深度森林的卫星ACS执行机构与传感器故障识别

Fault identification of actuators and sensors of satellite attitude control systems based on deep forest algorithm

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