With various modeling technologies applied, the sensor fault detection and isolation scheme based on the decentralized model (also referred to as dedicated observer scheme) becomes a popular approach for sophisticated systems. However, the commonly used modeling approach in many literatures that directly takes measurement values as model inputs may result in residual crosstalks and even false alarms. In this paper, the traditional decentralized model scheme is analyzed and a novel scheme based on the time window interactive prediction structure is proposed. Then, the Elman neural network is applied to model identification due to its nonlinear approximation and online learning properties. Finally, Simulations for comparison using the decoupled longitudinal motion model of some airplane are performed, and the results show that the proposed scheme has higher detection speed, lower false alarm rate and less undetected faults.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    An improved decentralized model for sensor fault detection and isolation demonstrated on an airplane system


    Contributors:


    Publication date :

    2015-03-01


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Airplane flutter demonstrated

    Engineering Index Backfile | 1929


    Spacecraft Fault Detection & Isolation System Design using Decentralized Analytical Redundancy

    Indra, S. / Trave-Massuyes, L. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2012


    STOL demonstrated feasible for large cargo BLC airplane

    Dickerman, F.N. / Branson, C.F. | Engineering Index Backfile | 1961


    Agent-Based Cooperative Decentralized Airplane-Collision Avoidance

    Šišlák, D / Volf, P / Pěchouček, M | IEEE | 2011