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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Ling, Bo (Autor:in) / Khonsari, Michael (Autor:in) / Mesgarnejad, A. (Autor:in) / Hathaway, Ross (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2009-03-01


    Format / Umfang :

    1026731 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Online adaptive hidden Markov model for multi-tracker fusion

    Vojir, Tomas / Matas, Jiri / Noskova, Jana | British Library Online Contents | 2016


    Online adaptive hidden Markov model for multi-tracker fusion

    Vojir, Tomas / Matas, Jiri / Noskova, Jana | British Library Online Contents | 2016


    Online adaptive hidden Markov model for multi-tracker fusion

    Vojir, Tomas / Matas, Jiri / Noskova, Jana | British Library Online Contents | 2016


    Online Identification of Hidden Semi-Markov Models

    Azimi, M. / Nasiopoulos, P. / Ward, R. K. et al. | British Library Conference Proceedings | 2003