Aircraft predictive maintenance automation and optimisation: Synthesised landing gear datasets

Stanton, Izaak and Munir, Kamran and Ikram, Ahsan and Elbakry, Murad (2023) Aircraft predictive maintenance automation and optimisation: Synthesised landing gear datasets. UWE https://researchdata.uwe.ac.uk/id/eprint/717/

Brief summary of project

In the aviation industry, predictive maintenance is vital for ensuring the safe and efficient operation of aircraft. However, the amount of open data available for research is limited due to the proprietary nature of aircraft data. In this work, time-series datasets are synthesised by training on real Airbus datasets from landing gear systems. These datasets contain no proprietary information, but maintain the shape and patterns present in the original, making them suitable for testing novel PdM models. They can be used by researchers outside of the industry to explore a more diverse selection of aircraft systems, and the proposed methodology can be replicated by industry data scientists to synthesise and release more data to the public. The results of this study demonstrate the feasibility of using these datasets to train predictive maintenance models for related industry problems.