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

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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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
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: https://usir.salford.ac.uk/id/eprint/2549

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