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 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 Detectability/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.
|Autori:||NAPOLITANO M; CASDORPH V; NEPPACH C; INNOCENTI M; SILVESTRI G|
|Titolo:||Online Learning Neural Architectures and Cross-Correlation Analysis for Actuator Failure Detection and Identification|
|Anno del prodotto:||1996|
|Digital Object Identifier (DOI):||10.1080/00207179608921851|
|Appare nelle tipologie:||1.1 Articolo in rivista|