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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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.
|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|
|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|
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