Prediction of Non-Routine Tasks Workload for Aircraft Maintenance with Supervised Learning

H. Li*, M.J. Ribeiro, Bruno F. Santos, I. Tseremoglou

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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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.
Original languageEnglish
Title of host publicationProceedings of the AIAA SCITECH 2024 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc. (AIAA)
Number of pages9
ISBN (Electronic)978-1-62410-711-5
DOIs
Publication statusPublished - 2024
EventAIAA SCITECH 2024 Forum - Orlando, United States
Duration: 8 Jan 202412 Jan 2024

Conference

ConferenceAIAA SCITECH 2024 Forum
Country/TerritoryUnited States
CityOrlando
Period8/01/2412/01/24

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