AI 中文总结
该研究提出符合EASA及Eurocae ED-324标准的长短期记忆循环神经网络重量估计器，利用空客在役机队数据，可在传统航电计算机上实现机载部署用于关键告警功能。
AI 中文摘要
本文聚焦于一种新型监督机器学习模型的实现，用于估计直升机起飞时的重量，该模型利用了空客全球在役机队的海量数据集。研究详细阐述了符合欧洲航空安全局（EASA）机器学习应用概念文件及现行欧洲民用航空设备组织（Eurocae）ED-324标准的学习保证流程。我们针对长短期记忆循环神经网络提出了一系列机器学习要求、机器学习模型描述及其实现方案，最后对实现的要求进行了验证。该实现在传统航空电子计算机上得到了验证，适用于将开发的机器学习模型重量估计器部署到机载目标上，用于机载告警等关键功能。
英文摘要
This paper focuses on the implementation of a novel supervised Machine Learning model for estimating helicopter weight during takeoff, utilizing extensive datasets from Airbus's global in-service fleet. The study details a learning assurance process aligned with the EASA concept paper for machine learning application, and with the on-going Eurocae ED-324. We propose a set of Machine Learning Requirements, a Machine Learning Model Description, and its implementation for a long short-term memory recurrent neural network. Finally, we verify the requirements on the implementation. Demonstrated on legacy avionics computers, the implementation is suitable for the deployment of the developed Machine Learning Model weight estimator on airborne targets for critical functions such as on-board alerting.
Journal refThe 2026 Annual Forum and Technology Display (Forum 82), The Future of Vertical Flight (VFS), May 2026, Palm Beach Florida, USA, United States
