电子测量与仪器学报

周登极,刘巧珍,岳梦云,黄大文,王煜林.基于可解释模型的火箭推力故障辨识与轨迹预测方法[J].电子测量与仪器学报,2023,37(11):72-80

基于可解释模型的火箭推力故障辨识与轨迹预测方法

Method for thrust fault identification and trajectory prediction of launch vehicle based on interpretable machine learning model

DOI：

中文关键词: 可解释机器学习模型注意力机制推力下降故障故障严重程度轨迹预测

英文关键词:interpretable machine learning model attention mechanism thrust drop fault fault severity trajectory prediction

基金项目:

作者	单位
周登极	1. 上海交通大学动力机械与工程教育部重点实验室
刘巧珍	2. 北京宇航系统工程研究所
岳梦云	2. 北京宇航系统工程研究所
黄大文	1. 上海交通大学动力机械与工程教育部重点实验室
王煜林	1. 上海交通大学动力机械与工程教育部重点实验室

Author	Institution
Zhou Dengji	1. Key Laboratory for Power Machinery and Engineering of Ministry of Education
Liu Qiaozhen	2. Beijing Institute of Astronautical Systems Engineering
Yue Mengyun	2. Beijing Institute of Astronautical Systems Engineering
Huang Dawen	1. Key Laboratory for Power Machinery and Engineering of Ministry of Education
Wang Yulin	1. Key Laboratory for Power Machinery and Engineering of Ministry of Education

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中文摘要:

针对运载火箭飞行过程中的强非线性和高不确定性问题,以及火箭推力下降故障对飞行过程可靠性和安全性的重大影响,基于注意力机制提出一种可解释机器学习模型以提高火箭推力下降故障检测、故障发动机定位、故障程度估计、以及故障后轨迹预测的准确性和鲁棒性,使用注意力层提取高维时序飞行监测数据的特征,以特征矩阵简洁表达高维时序数据,进而采用自注意力及全连接网络预测推力下降发生的位置和推力下降程度,并通过长短期记忆单元对特征向量进行解码实现未来时段内飞行轨迹准确预测。在火箭推力下降数据集上对提出的模型进行测试,验证了模型的有效性。结果表明,提出的模型的故障定位准确率为 96. 0%,故障严重程度估计精度为 94. 7%,轨迹预测平均误差为 0. 94%,提出的模型在推力下降故障模式中具有良好的应用效果。

英文摘要:

Aiming at the strong nonlinearity and high uncertainty in the flight process of launch vehicle, and the significant impact of thrust descent faults on the reliability and safety, an interpretable machine learning model based on attention mechanism is proposed to improve the accuracy and robustness of thrust descent fault detection, fault engine location, fault degree estimation, and trajectory prediction after faults. The attention layer is used to extract the features of the high-dimensional time series flight monitoring data, and the feature matrix is used to express the high-dimensional time series data succinctly. Then the self-attention and fully connected network are used to predict the position and degree of thrust descent, the feature vector is decoded by the long-term and short-term memory unit to realize the accurate prediction of flight trajectory. The proposed integrated model is tested on the thrust descent data set to verify the effectiveness. The results show that the accuracy of the proposed model is 96. 0% for the fault location, the accuracy is 94. 7% for the fault severity estimation, and the average trajectory prediction error is 0. 94%. The proposed model has good application effect in thrust descent fault modes.

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