It is shown that, for a specific class of domains, a faulty sensor is no harder to diagnose than other system objects (and may even be easier), thereby offering a solution for at least a subset of the sensor validation problem. A prototype called LES (Liquid-oxygen Expert System) has been constructed, which may be the first system to diagnose from sensor data using model-based knowledge of structure and function. LES uses both structural and functional knowledge to find the expected state of all system objects, including sensors. In such domains it has been found that it is even economical to use functional relationships in place of structural knowledge. The approach is generally algorithmic rather than heuristic and represents uncertainties as sets of possibilities. Functional relationships are inverted to determine hypothetical values for potentially faulty objects and may include conditional functions not normally considered to have inverses. The model created for LES is appropriate for describing the function of its domain: the Space Shuttle's launch processing system.


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    Title :

    Diagnosis and sensor validation through knowledge of structure and function


    Additional title:

    Diagnose und Sensorueberpruefung durch Wissen ueber Struktur und Funktion


    Contributors:

    Published in:

    Publication date :

    1987


    Size :

    9 Seiten, 13 Quellen



    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English





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