On the Trustworthiness of Machine Learning Models in Health and Usage Monitoring of In-Service Helicopters

This paper proposes a first iteration towards a framework for enhancing the trustworthiness of machine learning in the health and usage monitoring of in-service helicopters. This bottom-up approach is based on our experience operating machine learning models for monitoring Airbus Helicopters' customer fleets. Key factors for improving trustworthy machine learning have been identified for both the development and execution phases, with specific methods defined for each enabler. These methods have been implemented in two use-cases involving machine learning models for regression tasks: monitoring the helicopter's main gearbox lubrication system, deployed in the FlyScan predictive maintenance service, and tracking the usage of the main rotor lead-lag damper loads. The results from both use cases show that confidence in machine learning model predictions can be effectively improved.