SFDIA of consecutive sensor faults using neural networks – demonstrated on a UAV

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Neural network based sensor fault detection, isolation and accommodation (NN-SFDIA) is becoming a popular alternative to traditional linear time-invariant model-based sensor fault detection, isolation and accommodation (SFDIA) schemes, such as observer-based methods. Their online training capabilities and ability to model complex nonlinear systems have attracted much research interest in the applications area of neural networks. In this article, we design an NN-SFDIA scheme to detect multiple sensor faults in an unmanned air vehicle (UAV). Model-based SFDIA is a direction of development in particular with UAVs where sensor redundancy may not be an option due to weight, cost and space implications. In this article, a maximum of three consecutive faults are assumed in the pitch gyro, normal accelerometer and angle of attack sensor of a nonlinear UAV model. Furthermore, a novel residual generator which is designed to minimise the false alarm rates and missed faults, is implemented. After 33 separate SFDIA tests implemented on a 1.6 GHz Pentium processor, the NN-SFDIA scheme detected all but three faults with a fast execution time of 0.55 ms per flight data sample.