Automation of predictive maintenance: An experimental framework for aircraft landing gear

Stanton, Izaak; Munir, Kamran; Ikram, Ahsan; El-Bakry, Murad

Abstract

Terabytes of data are recorded per flight by modern aircraft, providing a goldmine for predictive maintenance modeling, however, the required domain knowledge to build ML tools limits the number developed by airline manufacturers each year. Automated machine learning (AutoML) libraries can simplify model development, providing features such as automated preprocessing, model selection, and hyperparameter tuning to improve the efficiency and accessibility of the development workflow. This research presents an experimental analysis comparing industry‐selected machine learning models and a hand‐picked selection of automated machine‐learning tools. The selected models were evaluated against real and synthetic time series datasets for different Airbus landing gear components across six datasets. The traditional and automated models obtained comparable MAE and F1 scores on regression and classification problems, accordingly, demonstrating the effectiveness of their use in this field. Based on these findings, a robust framework is proposed to utilize automated ML to optimize predictive maintenance tool development. This research is a stepping stone towards greater use of automation for predictive maintenance and presents insights into the field and AutoML. By integrating greater automation, AutoML can exploit more of the available data and deskill the development process to enable non‐data scientists to produce health monitoring models for a more diverse pool of aircraft components.

• Journal Article Type: Article
• Acceptance Date: May 13, 2025
• Online Publication Date: Jun 8, 2025
• Publication Date: Jun 30, 2025
• Deposit Date: May 14, 2025
• Publicly Available Date: Jun 18, 2025
• Journal: Engineering Reports
• Electronic ISSN: 2577-8196
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 6
• Article Number: e70214
• DOI: https://doi.org/10.1002/eng2.70214
• Keywords: automated machine learning, predictive maintenance, machine learning, aircraft maintenance
• Public URL: https://uwe-repository.worktribe.com/output/14422443