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In this paper, we propose and develop a methodology for nonlinear systemshealth monitoring by modeling the damage and degradation mechanism dynamics as"slow" states that are augmented with the system "fast" dynamical states. Thisaugmentation results in a two-time scale nonlinear system that is utilized fordevelopment of health estimation and prediction modules within a healthmonitoring framework. Towards this end, a two-time scale filtering approach isdeveloped based on the ensemble Kalman filtering (EnKF) approach by takingadvantage of the model reduction concept. The performance of our proposedtwo-time scale ensemble Kalman filters is shown to be superior and lesscomputationally intensive in terms of the equivalent flop (EF) complexitymetric when compared to well-known particle filtering (PF) approaches. Ourproposed methodology is then applied to a gas turbine engine that is affectedby erosion of the turbine as the degradation phenomenon and damage mechanism.Extensive comparative studies are conducted to validate and demonstrate theadvantages and capabilities of our proposed framework and methodology.

Ensemble Kalman Filters (EnKF) for State Estimation and Prediction of Two-time Scale Nonlinear Systems with Application to Gas Turbine Engines