Procedure for tracking damage evolution and predicting remaining useful life with application to an electromechanical experimental system

A general method for tracking the evolution of hidden damage processes and predicting remaining useful life is presented and applied experimentally to an electromechanical system with a failing supply battery. The fundamental theory for the method is presented. In this theory, damage processes are viewed as occurring in a hierarchical dynamical system consisting of ”fast”, directly observable subsystem coupled with a ”slow”, hidden subsystem describing damage evolution. In the algorithm, damage tracking is achieved using a two-time-scale modeling strategy based on phase space reconstruction. Using the reconstructed phase space of the reference (undamaged) system, short-time predictive models are constructed. Fast-time data from later stages of damage evolution of a given system are collected and used to estimate the short time reference model prediction error or a tracking metric. The tracking metric is used as an input to a nonlinear recursive filter, the output of which provides an estimate of the current damage (or, equivalently, health) state. Estimates of remaining useful life (or, equivalently, time to failure) are obtained recursively using the current damage state estimates under the assumption of a particular battery voltage evolution model. In the experimental application, the method is shown to accurately estimate both the battery state and the time to failure throughout the whole experiment.