Fusing Competing Prediction Algorithms for Prognostics

Two fundamentally different approaches can be employed to estimate remaining life in faulted components. One is to model from first principles the physics of fault initiation and propagation. Such a model must include detailed knowledge of material properties, thermodynamic and mechanical response to loading, and the mechanisms for damage creation and growth. Alternatively, an empirical model of condition-based fault propagation rate can be developed using data from experiments in which the conditions are controlled or otherwise known and the component damage level is carefully measured. These two approaches have competing advantages and disadvantages. However, fusing the results of the two approaches produces a result that is more robust than either approach alone. In this paper, we introduce an approach to fuse competing prediction algorithms for prognostics. Results presented are derived from rig test data wherein multiple bearings were first seeded with small defects, then exposed to a variety of speed and load conditions similar to those encountered in aircraft engines, and run until the ensuing material liberation accumulated to a predetermined damage threshold or cage failure, whichever occurred first