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A prognosis model for wear prediction based on oil-based monitoring

Wang, W 2006, 'A prognosis model for wear prediction based on oil-based monitoring' , Journal of the Operational Research Society , pp. 1-7.

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    Abstract

    This paper reports on the development of a wear prediction model based on stochastic filtering and hidden Markov theory.It is assumed that observations at discrete time points are available such as metal concentrations from oil-based monitoring, which are related to the true underlying state of the system which is unobservable.The system state is represented by a generic term of wear which is modelled by a continuous hidden Markov Chain using a Beta distribution.We formulated a recursive model to predict the current and future system state given past observed monitoring information to date.The model is useful to wear-based monitoring such as oil analysis. Numerical examples are presented in the paper based on simulated and real data.

    Item Type: Article
    Uncontrolled Keywords: wear; stochastic filtering; hidden Markov chain; oil analysis; prediction; beta distribution
    Themes: Subjects / Themes > Q Science > QA Mathematics > QA275 Mathematical Statistics
    Subjects outside of the University Themes
    Schools: Colleges and Schools > College of Business & Law > Salford Business School > Operations and Global Logistics Management
    Journal or Publication Title: Journal of the Operational Research Society
    Publisher: Palgrave Macmillan
    Refereed: Yes
    ISSN: 0160-5682
    Funders: Engineering and Physical Sciences Research Council (EPSRC)
    Depositing User: W Wang
    Date Deposited: 24 Nov 2009 11:48
    Last Modified: 20 Jan 2014 20:53
    URI: http://usir.salford.ac.uk/id/eprint/2549

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