Assessing a Statistical and a Set-based Approach for Remaining Useful Life Prediction

In this paper, an assessment of two methods of uncertainty quantification in prognostics is undertaken. The two methods, the Inverse First Order Reliability Method (IFORM) and set-based reachability analysis for prognostics are considered. The IFORM approach permits the generation of confidence bounds that allows for the calculation of RUL values corresponding to the specified user-defined probability levels. On the other hand, uncertainty quantification can be achieved by means of set-based reachability analysis. A Zono-topic Kalman filter (ZKF) is proposed to take into account a damage-model such that at each propagation time, with the estimated state (degradation) and its uncertainty, a propagation of zonotopic sets can be produced. Coming from two different schools of thought, the statistical and set-based theory, both schemes are explored and tested on a case study in simulation.