Addressing Gearbox Health Monitoring Challenges for Helicopters: A Machine Learning Approach

Abstract

Abstract The transmission gearbox of military helicopters, such as the H225M, experiences intense dynamic loads, leading to the detachment of ferromagnetic particles, often due to wear or fatigue. This poses safety risks, as excessive particle detachment demands stringent maintenance. To address this, the study applies machine learning algorithms to predict particle detachment using data from the Flight Data Recorder and Health and Usage Monitoring System. The approach aims to mitigate operational challenges faced by the Brazilian H225M fleet while considering aviation safety criteria and the pre-processing needs for an effective machine learning application.

Keywords

• Machine Learning
• flight safety criteria
• helicopter transmission system
• Airbus H225M
• failure prediction