基于深度森林的卫星ACS执行机构与传感器故障识别

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。

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

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.