This paper introduces a method to detect a fault associated with critical components/subsystems of an engineered system. It is required, in this case, to detect the fault condition as early as possible, with specified degree of confidence and a prescribed false alarm rate. Innovative features of the enabling technologies include a Bayesian estimation algorithm called particle filtering, which employs features or condition indicators derived from sensor data in combination with simple models of the system's degrading state to detect a deviation or discrepancy between a baseline (no-fault) distribution and its current counterpart. The scheme requires a fault progression model describing the degrading state of the system in the operation. A generic model based on fatigue analysis is provided and its parameters adaptation is discussed in detail. The scheme provides the probability of abnormal condition and the presence of a fault is confirmed for a given confidence level. The efficacy of the proposed approach is illustrated with data acquired from bearings typically found on aircraft and monitored via a properly instrumented test rig.


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    Title :

    A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection


    Contributors:
    Zhang, Bin (author) / Sconyers, C. (author) / Byington, C. (author) / Patrick, R. (author) / Orchard, M.E. (author) / Vachtsevanos, G. (author)

    Published in:

    Publication date :

    2011


    Size :

    8 Seiten, 24 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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