Methodologies for system-level remaining useful life prediction

• Prognostics of systems with multiple degrading components are explored.
• The proposed system-level prognostics encompass estimation and prediction steps.
• A PF approach is developed for system state and parameter degradation estimations.
• Stochastic simulation and inverse-FORM are extended to system-level prediction.
• The computational complexity and the accuracy of the proposed methods are compared.

While most prognostics approaches focus on accurate computation of the degradation rate and the remaining useful life (RUL) of individual components, it is the rate at which the performance of subsystems and systems degrade that is of greater interest to the operators and maintenance personnel of these systems. We develop a comprehensive methodology for system-level prognostics under different forms of uncertainty in this paper. Our approach combines an estimation scheme with a prediction scheme to compute the RUL as a stochastic distribution over the life of the system. We compare two prediction methods: (1) stochastic simulation and (2) the inverse first order reliability method (inverse-FORM). We compare the computational complexity and the accuracy of the two approaches using a case study of a system with several degrading components.