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

W Wang ()
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W Wang: University of Salford

Journal of the Operational Research Society, 2007, vol. 58, issue 7, 887-893

Abstract: 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.

Keywords: wear; stochastic filtering; hidden Markov chain; oil analysis; prediction; beta distribution (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (8)

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DOI: 10.1057/palgrave.jors.2602185

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