Enabling Accurate and Timely Prognostics for Aircraft Turbofan Engines

The aviation industry relies on scheduled maintenance performed on aircraft engines, which ensures safety but incurs significant costs during routine inspections. Traditional preventative maintenance may introduce issues during inspections, leading to delays and cancellations. Predictive maintenance, powered by edge computing, offers a more efficient solution to predict engine failures, optimize schedules, and enhance safety. This paper explores the development and evaluation of a predictive model based on the Gradient-Boosting Regression Tree (GBRT) algorithm for turbofan engine prognostics. Our study uses a synthetic dataset to evaluate the performance of the proposed model through various external conditions and internal configurations. Through this analysis, we compare our model's performance to existing solutions and propose a new benchmarking metric, Margin-Adjusted Reliability Score (MARS), to better assess the applicability and effectiveness of predictive maintenance models in real-world scenarios.