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首页> 外文会议>55th International Astronautical Congress 2004 vol.13 >THE USE OF ARTIFICIAL NEURAL NETWORKS TO MODEL LAUNCH VEHICLE RELIABILITY
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THE USE OF ARTIFICIAL NEURAL NETWORKS TO MODEL LAUNCH VEHICLE RELIABILITY

机译：使用人工神经网络来模型化汽车可靠性

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Expendable launch vehicles currently have a reliability of 92%. The failures that do occur cost millions of dollars in spacecraft replacement, lost revenue, and other expenses. These costs are passed on in higher insurance rates and launch vehicle price. If the reliability of the launch vehicles could be better predicted, the overall cost of launching payloads into space would decrease. This study used artificial neural networks to model the overall reliability of a launch vehicle so that the results of the launch could be predicted. Neural networks have shown success in modeling reliability of complex systems, but they have never before been used to model launch vehicles. Two neural network architectures—MLP and fuzzy ARTMAP—were trained on historical launch data of Atlas, Delta, and Titan vehicles. The networks were tested on their ability to generalize to new data. It was found that the network architectures have different success rates predicting launch vehicle failures. Application of the networks in real-time during the vehicle launch countdown is a real possibility for the future.
机译：消耗性运载火箭目前的可靠性为92％。确实发生的故障在更换航天器上造成了数百万美元的损失，收入损失以及其他费用。这些费用在较高的保险费率和运载工具价格中转移。如果可以更好地预测运载火箭的可靠性，则将有效载荷运载到太空的总成本将降低。这项研究使用人工神经网络对运载火箭的整体可靠性进行建模，以便可以预测运载火箭的结果。神经网络在对复杂系统的可靠性进行建模方面已显示出成功，但神经网络从未被用于对运载火箭进行建模。在Atlas，Delta和Titan车辆的历史发射数据上训练了两种神经网络架构（MLP和模糊ARTMAP）。测试了网络对泛化到新数据的能力。发现网络架构具有不同的成功率，可以预测运载火箭的故障。在车辆启动倒计时期间实时应用网络是未来的真正可能性。

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