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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    Title :

    Probabilistic model based algorithms for prognostics


    Contributors:
    He, H. (author) / Shenliang Wu, (author) / Banerjee, P. (author) / Bechhoefer, E. (author)

    Published in:

    Publication date :

    2006-01-01


    Size :

    316525 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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