基于功耗残差的航天器 CMG 退化特征提取方法

Control moment gyro (CMG) is the actuator for the attitude control of large spacecraft. In order to evaluate the performance degradation state of CMG, a convolutional neural network (CNN) and residual power consumption-based degradation feature extraction method is proposed. The high-precision control of the CMG control system makes it difficult to extract degradation features from the operational state of the CMG rotor. To solve this problem, a CNN model is introduced to establish the mapping between CMG operating state parameters and motor power consumption, and the degradation feature is defined as the residual error between the model output and actual power consumption of the motor in the degraded state. For approach validation, an accelerated life test dataset of a real CMG was used. The results show that the constructed degradation feature can reflect the performance degradation of the CMG rotor bearing.