Use of Machine Learning Techniques to Support Future Ship-Helicopter Operations Research; an Initial Investigation

Daniel Newton-Young; Mark White; Peter L Green

doi:10.4050/F-0080-2024-1378

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Use of Machine Learning Techniques to Support Future Ship-Helicopter Operations Research; an Initial Investigation

Daniel Newton-Young, Mark White, Peter L Green, University of Liverpool

May 7, 2024

https://doi.org/10.4050/F-0080-2024-1378

Abstract:
This paper reports on the initial implementation of Machine Learning (ML) for predicting the workload experienced by a pilot when performing a recovery to a naval ship. Pilots classify their workload for each landing by providing a subjective rating, which is used to determine the ship-helicopter operating limit (SHOL). Different workload metrics have been trialed to bridge the gap between pilot subjective ratings and objective flight data. With hundreds of different helicopter, ship and airwake parameters available to examine, ML provides an approach to understanding the complex interactions between these variables. This paper looks at the initial results obtained by applying ML techniques to train a classification algorithm with pilot control input data. Preliminary results showed 77.14% accuracy when training a Linear Discriminant algorithm to predict pilot workload from cyclic, collective, and pedal input data.