Detection of multiple sensor faults using neural networks-demonstrated on an unmanned air vehicle (UAV) model

Abstract

Model-based sensor fault detection, isolation and accommodation (SFDIA) is a direction of development in particular with small UAVs where sensor redundancy may not be an option due to weight, cost and space implications. SFDIA via neural networks (NNs) have been proposed over the years due to their nonlinear structures and online learning capabilities. However few applications have considered multiple sensor faults in fixed-wing UAVs where full autonomy is most needed. In this paper an Extended Minimum Resource Allocating Network (EMRAN) Radial Basis Function (RBF) NN is chosen for modelling purposes due to its ability to adapt well to nonlinear environments while maintaining high computational speeds. After 30 separate SFDIA tests implemented on a 1.6 GHz Pentium processor, the NN-SFDIA scheme detected all but 2 faults and the NN processing time was 97% lower than the flight data sampling time.