Aircraft engine health management via stochastic modelling of flight data interrelations

A novel engine health management (EHM) scheme is introduced. It uses flight-level, instead of thermodynamic, data to cost-effectively augment the onboard EHM redundancy. For a nominal healthy aircraft, fault-sensitive interrelations among flight data are globally modelled inside a flight regime via Constant-Coefficient Pooled Nonlinear AutoRegressive with eXogenous (CCP-NARX) excitation representations. Single or sequential engine faults perturb these interrelations. Statistically evaluating the perturbation-induced effects draws reliable conclusions on the engineʼs health. Validation and comparisons with Kalman filter-based alternatives are made throughout the regime under various operational conditions.