In this paper, two prognostics algorithms to accurately predict the state of a complex system as a function of time are presented. The algorithms are developed based on a hidden semi-Markov model (HSMM) and validated on a real-world helicopter rotor track and balance prognosis problem. It is shown that the developed prognostic algorithms provide a good performance in predicting the time to the next required rotor track and balance action for two different application scenarios.


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    Titel :

    Probabilistic model based algorithms for prognostics


    Beteiligte:
    He, H. (Autor:in) / Shenliang Wu, (Autor:in) / Banerjee, P. (Autor:in) / Bechhoefer, E. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2006-01-01


    Format / Umfang :

    316525 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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