Data augmentation for predictive maintenance: Synthesising aircraft landing gear datasets

Abstract

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 Gretel.ai 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.