Multiple Damage Progression Paths in Model-based Prognostics

Compo- nent wear is driven by several different degradation phenom- ena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics method- ology using particle filters, in which the problem of charac- terizing multiple damage progression paths is cast as a joint state-parameter estimation problem.

The estimate is repre- sented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model- based prognostics algorithms.