We present a feasibility analysis for the development of an online ball bearing fault detection and identification method which can effectively classify various fault stages related to the contact in the coated ball bearings using vibration measurements. To detect ball bearing faulty stages, we have developed new degree of randomness (DoR) analysis methods using Shannon entropy and random covariance matrix norm theory. To classify the fault stages, we have further developed a set of stochastic models using Gaussian Mixture Hidden Markov Model (GM-HMM) theory. Test results have shown that our algorithms can predict bearing failures without using actual failure data.


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    Title :

    Online coated ball bearing health monitoring using degree of randomness and Hidden Markov Model


    Contributors:


    Publication date :

    2009-03-01


    Size :

    1026731 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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