Sensor fault detection and accommodation using neural networks with application to a non-linear unmanned air vehicle model

Abstract

Model-based fault detection and isolation (FDI) systems have become a crucial step towards autonomy in aerospace engineering. Traditional methods such as observer-based methods have already been developed and widely applied. Novel approaches make use of online learning neural networks (NN) which have seen an increase in FDI applications over the years. However, few publications consider FDI applications to unmanned air vehicles (UAV) where high levels of autonomy are required. This article demonstrates such an application, where an extended minimum resource allocation network radial basis function (RBF) NN is used for modelling purposes. A novel residual generation approach is also presented and found to outperform a conventional approach by reducing the number of false alarms and missed faults. All tests are carried out in simulation where single sensor faults are assumed to occur in the pitch gyro of a non-linear UAV model.