Remaining Useful Life prediction method using an observer and statistical inference estimation methods

In this paper, we propose an approach for failure prognosis. The approach deals with a closed-loop control system in which the actuator stochastically degrades through time. The degradation of a system disturbs and affects its characteristic parameters. This is reflected by a change in one or more of them. The latter may remain partially or totally hidden given that the type of control. The aim of this work was to estimate online the duration before the system performance requirement is no longer met. This without adding sensors. The proposed approach is based on the system behavior model. The models describing the dynamics of the parameters have been assumed to be known a priori, but degradation is assumed to be unmeasurable. It was conducted in two phases: the first used the data available on this system to estimate unmeasured states and relevant parameters which are able to characterize system performance. To carry out this phase, we used an observer. In the second phase, to estimate when the desired performance will no longer be met over a specified mission, the historical states and parameters obtained in the first phase were exploited. Thus, in order to identify the models describing the parameter dynamics, statistical inference estimation methods such as the maximum likelihood method and the Bayesian estimation were used. To illustrate the performances of the approach, a simulated tank level control system was used.