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

TL;DR

This paper introduces a two-time scale ensemble Kalman filter approach for nonlinear system health monitoring, demonstrating improved accuracy and efficiency in predicting degradation in gas turbine engines.

Contribution

The paper develops a novel two-time scale EnKF method for nonlinear health monitoring, leveraging model reduction for enhanced performance and computational efficiency.

Findings

Superior performance over particle filters in accuracy and computational cost

Effective application to gas turbine engine degradation prediction

Validated through extensive comparative studies

Abstract

In this paper, we propose and develop a methodology for nonlinear systems health monitoring by modeling the damage and degradation mechanism dynamics as "slow" states that are augmented with the system "fast" dynamical states. This augmentation results in a two-time scale nonlinear system that is utilized for development of health estimation and prediction modules within a health monitoring framework. Towards this end, a two-time scale filtering approach is developed based on the ensemble Kalman filtering (EnKF) approach by taking advantage of the model reduction concept. The performance of our proposed two-time scale ensemble Kalman filters is shown to be superior and less computationally intensive in terms of the equivalent flop (EF) complexity metric when compared to well-known particle filtering (PF) approaches. Our proposed methodology is then applied to a gas turbine engine that is affected by erosion of the turbine as the degradation phenomenon and damage mechanism. Extensive comparative studies are conducted to validate and demonstrate the advantages and capabilities of our proposed framework and methodology.