Towards On-Board Implementation of ML-Based Helicopter Weight Estimator
Summary
The paper proposes a supervised machine learning model for estimating helicopter weight during takeoff, using data from Airbus's fleet, and details its implementation for on-board use with regulatory compliance.
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# Towards On-Board Implementation of ML-Based Helicopter Weight Estimator Source: [https://arxiv.org/abs/2608.19210](https://arxiv.org/abs/2608.19210) [View PDF](https://arxiv.org/pdf/2608.19210) > 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\. ## Submission history From: Claire Pagetti \[[view email](https://arxiv.org/show-email/6b6e617c/2608.19210)\] \[via CCSD proxy\] **\[v1\]**Fri, 12 Jun 2026 11:42:28 UTC \(1,376 KB\)
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