Terabytes of data are recorded per flight by modern aircraft, providing a goldmine for predictive maintenance modelling, however, the required domain knowledge to build machine learning 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 utilise automated machine learning to optimise 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.