Prognostics Health Management System based on Hybrid Model to Predict Failures of a Planetary Gear Transmission

Abstract

Health condition monitoring has developed over several years. However, in the area of health assessment algorithms, most of the research has focused on data-driven approaches that do not rely on the knowledge of the physics of the system, while physics-based model (PbM) approaches which rely on the understanding of the system and the degradation mechanisms, are more limited and have the potential to provide more robust predictions due to the understanding of the failure mode phenomena. This paper proposes a Physics-based Model approach to detect incipient metal-metal contact and fatigue degradation of a planetary transmission of an aircraft. Both models are integrated in a realtime Prognostics Health Management (PHM) system that calculates the Remaining Useful Life (RUL) of the component. This tool also incorporates the decision-making process that is performed in the aircraft to connect/disconnect the transmission. A theoretical hybrid model that fuses a machine learning approach with the Physics-based approach to obtain a more robust prediction is also proposed.