Challenges of Machine Learning for eVTOL Reliability and Safety
Bibliographic record
Abstract
The increasing number of requests for type certification received by the European Union Safety Agency on Vertical Takeoff and Landing (VTOL) aircraft attests to the expansion of frontiers in Urban Air Mobility (UAM). In addition, it has revealed the interest of traditional airplane and helicopter manufacturers in this new technology, all the while highlighting the emergence of new players developing their respective versions of electric-powered VTOLs (eVTOL). The perspective of eVTOLs going into service in the coming years for the transport of passengers raises new safety concerns. Indeed, it is necessary to ensure the reliability and safety aspects of those aircraft systems that will be flying under new operational missions, differing from current fixed wing (airplanes) and rotorcraft (helicopters) aircraft. At the same time, the evolution of aircraft systems monitoring technology is making it possible to acquire increasing amounts of data. The high complexity of new systems, combined with the huge amount of data provided, can make the decision-making process more difficult for pilots. Machine learning makes it possible to evaluate this data and improve reliability and safety. Even as the number of aeronautical accidents has decreased over the last years, 60–80% of those accidents are the result of human failure. In the initial implementation and operation stages of eVTOLs, machine learning (ML) can support pilots by using aircraft data to predict system failures and contribute to improve reliability and safety. Then, at an advanced stage of eVTOL operation, ML may help reduce human interaction with the aircraft, paving the way toward fully autonomous aircraft. The association of ML with technologies such as Digital Twins and 6G networks has the potential to enable safe and reliable autonomous flight. However, the introduction of eVTOLs will also increase air traffic in highly populated areas and thus needs to be studied to support the incorporation of the future autonomous aircraft. This paper addresses the main challenges for the incorporation of ML in the upcoming eVTOL fleet and its potential contribution to aircraft systems reliability and safety. It also explores the need for the use of ML techniques in a more autonomous air traffic management systems the face of increased air traffic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".