Towards On-Board Implementation of ML-Based Helicopter Weight Estimator
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Computer Science > Machine Learning
Title:Towards On-Board Implementation of ML-Based Helicopter Weight Estimator
Abstract: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.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.19210 [cs.LG] |
| (or arXiv:2608.19210v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.19210
arXiv-issued DOI via DataCite
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| Journal reference: | The 2026 Annual Forum and Technology Display (Forum 82), The Future of Vertical Flight (VFS), May 2026, Palm Beach Florida, USA, United States |
Submission history
From: Claire Pagetti [view email] [via CCSD proxy][v1] Fri, 12 Jun 2026 11:42:28 UTC (1,376 KB)
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