Fault Injection for Synthetic Data Generation in Aircraft: A Simulation-Based Approach

The safety of aircraft heavily depends on the in-tegrity of the Landing Gear System (LGS). However, gathering real-world fault data to support effective Prognostic and Health Management (PHM) practices presents significant challenges. This work proposes a novel methodology for generating synthetic fault data using a multi-physics Simscape model of a landing gear deployment/retraction mechanism. The model incorporates specialized fault blocks designed to replicate various hydraulic failure modes, aiming to broaden the pool of fault data covering the most common failures. This approach promises to enhance maintenance strategies and facilitate the development of hybrid Model-Based and Data-Driven solutions. Ultimately, the results of this study will be used to understand the physics within the landing gear better and gather the necessary data to create an effective Digital Twin for predictive maintenance.