Wang, W 2007, 'A prognosis model for wear prediction based on oil-based monitoring' , Journal of the Operational Research Society, 58 (7) , pp. 887-893.
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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 |
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Themes: | Subjects / Themes > Q Science > QA Mathematics > QA275 Mathematical Statistics Subjects outside of the University Themes |
Schools: | Schools > Salford Business School > Salford Business School Research Centre |
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: | 15 Feb 2022 15:36 |
URI: | http://usir.salford.ac.uk/id/eprint/2549 |
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