航天器环境工程2025，Vol.42Issue(5)：566-573,8.DOI:10.12126/see.2025029
基于神经网络的1N推力器组件电磁阀关时间预测
Neural network-based prediction of solenoid valve closing time for a 1 N thruster assembly
摘要
Abstract
The switching performance of the solenoid valve in a 1 N thruster assembly is a critical factor influencing the reliability of satellite propulsion systems.Excessively long closing times observed during low-temperature tests can result in project delays and cost overruns.Since conventional testing methods are unable to detect anomalies in advance,a high-accuracy prediction model is essential for early risk identification.In this study,production test data from 194 sets of 1 N thruster assemblies were used to develop back propagation(BP)and convolutional neural network(CNN)models.Twenty-one key parameters of the solenoid valve were selected as inputs,with the low-temperature closing time as the output.The results indicate that the BP neural network model achieved significantly higher prediction accuracy than the CNN model,suggesting that its fully connected structure is better suited for small-scale,well-characterized engineering data.These findings demonstrate the strong potential of neural network algorithms for predicting the performance of aerospace components,providing data support for parameter optimization and risk mitigation,thereby improving development efficiency and product reliability.关键词
1N推力器组件/电磁阀关时间/预测模型/BP算法/卷积神经网络模型Key words
1 N thruster assembly/solenoid valve closing time/prediction model/back propagation(BP)algorithm/convolutional neural network(CNN)分类
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陈阳..基于神经网络的1N推力器组件电磁阀关时间预测[J].航天器环境工程,2025,42(5):566-573,8.
