The detection and diagnosis of SSME faults in an early stage is important in order to allow enough time for fault preventive or corrective measurements. Since most of the faults in a complex system like SSME develop rapidly, early detection and diagnosis of faults is critical for the survival of space vehicles. An expert system has been designed for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations. This paper describes an innovative machine learning approach which is employed for the automatic training of this expert system.


    Access

    Access via TIB

    Check availability in my library


    Export, share and cite



    Title :

    An expert system for fault diagnosis in a Space Shuttle main engine


    Contributors:
    Ali, Moonis (author) / Gupta, U. K. (author)

    Publication date :

    1990-07-01


    Type of media :

    Conference paper


    Type of material :

    No indication


    Language :

    English





    Fault diagnosis for the Space Shuttle main engine

    Duyar, Ahmet / Merrill, Walter | NTRS | 1992


    Fault diagnosis for the Space Shuttle main engine

    DUYAR, AHMET / MERRILL, WALTER | AIAA | 1992


    Object oriented fault diagnosis system for space shuttle main engine redlines

    Rogers, John S. / Mohapatra, Saroj Kumar | NTRS | 1990


    Embedded Expert System for Space Shuttle Main Engine Maintenance

    J. Pooley / W. Thompson / T. Homsley et al. | NTIS | 1987