Predicting the Remaining Useful Life of Landing Gear with Prognostics and Health Management (PHM)

Landing gear is an essential part of aircraft. However, the components landing are susceptible to degradation over life their operation, which can result in shimmy effect occurring during take-off and landing. In order reduce unplanned flight disruptions increase availability aircraft, predictive maintenance (PdM) technique investigated this study. This paper presents a case study on implementation health assessment prediction workflow for remaining useful (RUL) based prognostics management (PHM) framework currently in-service could significantly benefit fleet operators aircraft maintenance. Machine learning utilized develop indicator (HI) using data-driven approach, whereas time-series analysis (TSA) used predict its degradation. The models evaluated large volumes real sensor data from Finally, challenges implementing built-in PHM system next-generation outlined.