Print Email Facebook Twitter Prediction of Non-Routine Tasks Workload for Aircraft Maintenance with Supervised Learning Title Prediction of Non-Routine Tasks Workload for Aircraft Maintenance with Supervised Learning Author Li, H. (Student TU Delft) Ribeiro, M.J. (TU Delft Air Transport & Operations) Santos, Bruno F. (TU Delft Air Transport & Operations) Tseremoglou, I. (TU Delft Air Transport & Operations) Date 2024 Abstract Aircraft maintenance scheduling is a focus point for airlines. Maintenance is essential to ensure the airworthiness of aircraft, but it comes at the cost of rendering them unavailable for operations. In current operations, aircraft maintenance scheduling must often be updated to include time for non-routine and non-schedule tasks. These non-routine tasks can increase costs, maintenance workload, and uncertainty of the airlines’ operations. This research introduces a supervised learning framework designed to forecast future non-routine task workloads accurately, improving the accuracy of the planned maintenance schedule. This framework consists of two random forest predictors which estimate the amount of non-routine tasks and the number of future work hours that should be allocated in advance for potential non-routine tasks. Our approach produces highly reliable predictions by leveraging a robust dataset obtained from an international airline. The results show an average of 20% improvement versus an existing on-site sampling method. Furthermore, our in-depth analysis of prediction distributions enables the identification of the underlying causes of significant prediction errors, shedding light on the unpredictabilities inherent to non-routine tasks. To reference this document use: http://resolver.tudelft.nl/uuid:e8f28f08-e648-4a08-814e-f511359020a4 DOI https://doi.org/10.2514/6.2024-2529 Publisher American Institute of Aeronautics and Astronautics Inc. (AIAA) ISBN 978-1-62410-711-5 Source Proceedings of the AIAA SCITECH 2024 Forum Event AIAA SCITECH 2024 Forum, 2024-01-08 → 2024-01-12, Orlando, United States Part of collection Institutional Repository Document type conference paper Rights © 2024 H. Li, M.J. Ribeiro, Bruno F. Santos, I. Tseremoglou Files PDF li_et_al_2024_prediction_ ... arning.pdf 421.14 KB Close viewer /islandora/object/uuid:e8f28f08-e648-4a08-814e-f511359020a4/datastream/OBJ/view