Online learning neural architectures and cross-correlation analysis for actuator failure detection and identification

This paper describes a study related to the testing and validation of a neural-network based approach for the problem of actuator failure detection and identification following battle damage to an aircraft control surface. Online learning neural architectures, trained with the Extended Back-Propagation algorithm, have been tested under nonlinear conditions in the presence of sensor noise. In addition, a parametric study has been conducted that addresses the selection of 'near optimal' neural architectures for online state estimation purposes. The Failure Detect-ability/False Alarm Rate ratio problem has also been considered in this study. The testing of the approach is illustrated through typical highly nonlinear dynamic simulations of a high performance aircraft.