An adapted Brownion motion model for plant residual life prediction

This paper presents a stochastic degradation model for the residual life prediction of monitored plants using an adapted Brownian motion based approach with a drifting parameter. This model differs from other Brownian motion based approaches in that the drifting parameter of the degradation process is adapted to the history of monitored information. This treatment is performed by Kalman filtering so that the prediction becomes plant history dependent, which is a useful addition to the literature and practice. We also use a threshold distribution instead of the usual single line which is impractical in practice. We demonstrate the model using some examples and show that the model performs reasonably well and has the advantages of rapid updating.