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 de tection and identification following battle damage to an aircraft cont rol surface. Online learning neural architectures, trained with the Ex tended Back-Propagation algorithm, have been tested under nonlinear co nditions in the presence of sensor noise. In addition, a parametric st udy has been conducted that addresses the selection of 'near optimal' neural architectures for online state estimation purposes. The Failure Detectability/False Alarm Rate ratio problem has also been considered in this study. The testing of the approach is illustrated through typ ical highly nonlinear dynamic simulations of a high performance aircra ft.