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: | Wang W. |
Keywords: | markov processes, statistics: distributions |
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.