Reliability and Predictive Maintenance of PT6 Engines Using Machine Learning (Random Forests) within the Industry 4.0 Framework

Abstract

Aircraft engine reliability is critical, particularly in military operations where mission success and safety depend on optimal engine performance. The PT6 engines, used in Super Tucano aircraft by the Ecuadorian and Brazilian Air Forces, are renowned for their versatility and robustness. However, their operational demands necessitate advanced maintenance strategies to prevent failures, enhance safety, and minimize downtime. One of the challenges in developing such strategies lies in managing the uncertainties inherent in engine performance and degradation. Variations in operating conditions, environmental factors, and measurement noise introduce uncertainties that can complicate the prediction of failures and the estimation of Remaining Useful Life (RUL). This study addresses these challenges by incorporating a parametric analysis within the machine learning framework, specifically using Random Forests. This approach not only captures the complex relationships between operational parameters and engine degradation but also evaluates the sensitivity of predictions to variations in key inputs. By leveraging Industry 4.0 technologies, including Big Data Analytics and IoT, the study aims to enhance the robustness of predictive maintenance (PdM) models, ensuring operational readiness and cost-effectiveness in both military and civilian aviation contexts.