The detection, diagnosis, and accommodation of system failures or degradations is becoming increasingly more important in modern engineering problems. This paper presents a general framework for constructing automated fault diagnosis and accommodation architectures using on-line approximators and adaptation/learning schemes. In this framework, neural network models constitute an important class of on-line approximators. Changes in the system dynamics are monitored by an on-line approximation model, which is used not only for detecting but also for accommodating failures. A systematic procedure for constructing nonlinear estimation algorithms and stable learning schemes is developed and illustrated by a simulation example.


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    Title :

    An On-Line Approximation Approach to Fault Monitoring, Diagnosis and Accommodation


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    Aerospace Atlantic Conference & Exposition ; 1994



    Publication date :

    1994-04-01




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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