PROGNOSTIC FUSION FOR UNCERTAINTY REDUCTION (Preprint)

This paper describes how the fusion of two different prognostic approaches produces a result that is more accurate and has more narrow uncertainty bounds than either approach alone. The fused prognostic estimate can be calculated by using both a physics-based as well as a datadriven approach. The individual approaches can have a plurality of input sources such as component properties (e.g., material properties and usage properties), history of the component (current damage state and history of accumulated usage), future anticipated usage, damage propagation rates established during experiments, etc. Damage estimates are arrived at using sensor information such as oil debris monitoring data as well as vibration data. The method detects the onset of damage and triggers the prognostic estimator that projects the remaining life. Uncertainty, stemming from the variability observed during experiments, as well as modeling inaccuracies, are propagated to provide a distribution around the projected remaining life. It is desirable to keep the uncertainty interval as narrow as possible while truthfully considering their spread. 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.