用机器学习在机载计算机上实现直升机起飞重量实时估算。
Towards On-Board Implementation of ML-Based Helicopter Weight Estimator
- 基于LSTM的监督学习模型，利用空客真实飞行数据训练。
- 在老旧航电计算机上验证，误差满足航空安全要求。
- 适合用于飞行中重量告警等关键机载功能。
本文针对直升机起飞重量估算问题，提出一种基于监督学习的机器学习模型，利用空客全球在役机队的大量飞行数据进行训练。研究遵循EASA关于机器学习应用的概念文件及正在推进的Eurocae ED-324标准，制定了机器学习需求规范、模型描述，并实现了长短期记忆（LSTM）递归神经网络。最终在传统航电计算机上完成了模型部署与验证，结果表明该模型具备在机载系统中用于关键功能（如机上告警）的可行性与可靠性。
原文摘要 · Abstract (English)
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.
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