基于综合应力工作态试验和神经网络的CMG失效边界域预测

本文设计了一种可同时模拟真空热环境和CMG与航天器角动量交换工况的试验设备，提出了模拟在轨真空环境下温度、CMG框架转速、航天器转速3种应力的工作态试验方法，给出了适用于神经网络的CMG运行状态定量表达方法，利用少量试验数据和神经网络方法对工作极限转速矩阵、失效边界、失效边界域进行预测，分析了经验样本对预测结果的影响，以及各应力对其他应力工作域的耦合影响，并给出了预测结果的可信度分析方法。研究结果表明，所提出的方法可以更真实模拟CMG在轨工作状态的同时显著节省试验经费和时间，并具有较高的预测准确性和多应力工作场景适应性，对I和Ⅱ两类训练数据集分别获得100%和98.8%的预测正确率，给出了仅凭试验数据无法得到的55℃下的转速失效边界，并且可以内化试验数据背后的工程经验。

Prediction of CMG failure boundary domain based on combined stress test and neural network

This study designs the test equipment for simultaneous simulation of the vacuum thermal environment and angular momentum exchange conditions between a CMG (Control Moment Gyroscope) and a spacecraft, and proposes a combined stress working-state test method to simulate the in orbit vacuum temperature, CMG gimbal rotational speed and spacecraft rotational speed. A quantitative expression method for CMG running status suitable for a neural network model is applied. Based on relatively little amount of test results, the neural network model after training is used to predict the working limit rotational speed matrix, the failure boundary and the failure boundary domain. Furthermore, the influence of experience samples on the prediction results is analyzed, and the coupling effect of each stress on the working domain of other stresses investigated, and the predicted initial value by the neural network proposed to reflect the reliability of the prediction results. The results show that the presented method can not only simulate the real working-state better but also significantly save both test cost and time, in addition to high prediction accuracy and good adaptability under multi-stress working conditions. The prediction accuracy can reach up to 100% and 98.8% based on the two training data sets I and Ⅱ, respectively. The failure boundary of rotational speeds at 55 ℃ is given which cannot be obtained only via the test data, and the engineering experience behind the test data can be internalized.