Model-based Prognostics with Concurrent Damage Progression Processes

These models must account for the several different damage processes occurring simultaneously within a component. Each of these damage and wear processes contribute to the overall component degradation.

We develop a model-based prognostics methodology that consists of a joint state-parameter estimation problem, in which the state of a system along with parameters describing the damage progression are estimated, followed by a prediction problem, in which the joint state-parameter estimate is propagated forward in time to predict end of life and remaining useful life. The state-parameter estimate is computed using a particle filter, and is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management.