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Data augmentation for predictive maintenance: Synthesising aircraft landing gear datasets

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

Data augmentation for predictive maintenance: Synthesising aircraft landing gear datasets Thumbnail


Izaak Stanton

Ahsan Ikram

Murad El‐Bakry


In the aviation industry, predictive maintenance is vital to minimise Unscheduled faults and maintain the operational availability of aircraft. However, the amount of open data available for research is limited due to the proprietary nature of aircraft data. In this work, six time‐series datasets are synthesised using the DoppelGANger model trained on real Airbus datasets from landing gear systems. The synthesised 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 and effectiveness of using the DoppelGANger model from the library to generate new time series data that can be used to train predictive maintenance models for industry problems. These synthetic datasets were subject to fidelity testing using six metrics. The six datasets are available on the UWE Library service.

Journal Article Type Article
Acceptance Date May 29, 2024
Online Publication Date Jun 14, 2024
Deposit Date Jun 19, 2024
Publicly Available Date Jun 20, 2024
Journal Engineering Reports
Electronic ISSN 2577-8196
Publisher Wiley
Peer Reviewed Peer Reviewed
Keywords machine learning, aircraft maintenance, synthetic data, predictive maintenance, generative adversarial network
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