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

Wang, W

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.

Citation

Wang, W. (2007). A prognosis model for wear prediction based on oil-based monitoring. Journal of the Operational Research Society, 58(7), 887-893. https://doi.org/10.1057/palgrave.jors.2602185

• Journal Article Type: Article
• Online Publication Date: Mar 15, 2006
• Publication Date: Jul 1, 2007
• Deposit Date: Nov 24, 2009
• Journal: Journal of the Operational Research Society
• Print ISSN: 0160-5682
• Publisher: Palgrave Macmillan
• Peer Reviewed: Peer Reviewed
• Volume: 58
• Issue: 7
• Pages: 887-893
• DOI: https://doi.org/10.1057/palgrave.jors.2602185
• Keywords: wear; stochastic filtering; hidden Markov chain; oil analysis; prediction; beta distribution
• Publisher URL: http://dx.doi.org/10.1057/palgrave.jors.2602185
• Additional Information: Funders : Engineering and Physical Sciences Research Council (EPSRC)