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

Ihab Samy, Ian Postlethwaite, Da-Wei Gu

    Research output: Contribution to journalArticlepeer-review

    7 Citations (Scopus)

    Abstract

    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.
    Original languageEnglish
    Pages (from-to)2308-2327
    JournalInternational Journal of Control
    Volume83
    Issue number11
    DOIs
    Publication statusPublished - 2010

    Fingerprint

    Dive into the research topics of 'SFDIA of consecutive sensor faults using neural networks - demonstrated on a UAV'. Together they form a unique fingerprint.

    Cite this