Onboard engine FDI in autonomous aircrafts using compact stochastic nonlinear modelling of flight signal dependencies

An engine Fault Detection and Isolation (FDI) scheme for an autonomous aircraft is introduced. It relies on compact-in-size two-stage stochastic nonlinear modelling (valid for an entire flight regime) of the relationships among common flight variables. The key idea is that in the first stage, major nonlinearities in the dynamics are accounted for through the use of Constant Coefficient Pooled Nonlinear AutoRegressive with eXogenous (CCP-NARX) excitation representations. In the second stage any remaining information is modelled by low-order Constant Coefficient Pooled AutoRegressive Moving Average (CCP-ARMA) representations. These relationships (and the corresponding identified two-stage model) are valid for aircraft engines in healthy state. Thus, purposely designed statistical hypothesis tests are used to detect changes in these relationships due to fault occurrence. The scheme´s performance and robustness are assessed with flights conducted under various external conditions and commanded attitude settings.