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

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

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Article ID: iaor20082381
Country: United Kingdom
Volume: 58
Issue: 7
Start Page Number: 887
End Page Number: 893
Publication Date: Jul 2007
Journal: Journal of the Operational Research Society
Authors:
Keywords: markov processes, statistics: distributions
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.

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