Predictive Maintenance in Aircraft

Abstract

Predictive maintenance (PdM) is crucial in vital sectors like aviation. It uses data analytics and machine learning to optimize maintenance plans, lower operating costs, and eliminate unscheduled downtime. To create a PdM framework for forecasting the Remaining Useful Life (RUL) of aviation components, this study uses a publicly accessible aircraft engine dataset, which includes time-series data of operating settings and sensor readings. This study uses advanced machine learning model, such as Random Forest to show how PdM systems can predict component failures. The methodology includes data preparation, feature engineering, and rigorous evaluation utilizing measures. Though the dataset is not specific to Nepal, the findings show how this method can improve aviation safety in regions like Nepal, where difficult operating circumstances and a high frequency of aviation incidents demand better maintenance practices. This study raises the way for predictive maintenance’s increased adoption in the aviation industry by showing that it improves safety, lowers planned downtime, and promotes operational efficiency.