Journal of the Operational Research Society

, Volume 58, Issue 7, pp 887–893

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

Theoretical Paper

DOI: 10.1057/palgrave.jors.2602185

Cite this article as:
Wang, W. J Oper Res Soc (2007) 58: 887. doi:10.1057/palgrave.jors.2602185


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.


wear stochastic filtering hidden Markov chain oil analysis prediction beta distribution 

Copyright information

© Palgrave Macmillan Ltd 2006

Authors and Affiliations

  1. 1.University of SalfordSalfordUK
  2. 2.Harbin Institute of TechnologyHarbinChina

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