A new unsupervised pattern classifier is introduced for on-line detection of abnormality in features of vibration that are used for fault diagnosis of helicopter gearboxes. This classifier compares vibration features with their respective normal values and assigns them a value in (0, 1) to reflect their degree of abnormality. Therefore, the salient feature of this classifier is that it does not require feature values associated with faulty cases to identify abnormality. In order to cope with noise and changes in the operating conditions, an adaptation algorithm is incorporated that continually updates the normal values of the features. The proposed classifier is tested using experimental vibration features obtained from an OH-58A main rotor gearbox. The overall performance of this classifier is then evaluated by integrating the abnormality-scaled features for detection of faults. The fault detection results indicate that the performance of this classifier is comparable to the leading unsupervised neural networks: Kohonen's Feature Mapping and Adaptive Resonance Theory (ART2). This is significant considering that the independence of this classifier from fault-related features makes it uniquely suited to abnormality-scaling of vibration features for fault diagnosis.


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    Title :

    Unsupervised pattern classifier for abnormality-scaling of vibration features for helicopter gearbox fault diagnosis


    Additional title:

    Unüberwachtes Klassierverfahren für die Skalierung von Schwingungsabnormitäten bei der Störfalldiagnose von Hubschraubergetrieben


    Contributors:
    Jammu, V.B. (author) / Danai, K. (author) / Lewicki, D.G. (author)

    Published in:

    Machine Vibration ; 5 , 3 ; 154-162


    Publication date :

    1996


    Size :

    9 Seiten, 6 Bilder, 6 Tabellen, 18 Quellen



    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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