Markov Nonlinear System Estimation for Engine Performance Tracking

This paper presents a joint state and parameter estimation method for aircraft engine performance degradation tracking. Contrast to previously reported techniques on state estimation that view parameters in the state evolution model as constants, the method presented in this paper treats parameters as time-varying variables to account for varying degradation rates at different stages of engine operation. Transition of degradation stages and estimation of parameters are performed by particle filtering (PF) under the Bayesian inference framework. To address the sample impoverishment problem due to discrete resampling, which is inherent to PF, a continuous resampling strategy has been proposed, with the goal to improve estimation accuracy of PF. The algorithm has shown to be able to detect abrupt fault inception based on the residuals between the estimated results from the state evolution model and actual measurements. The developed technique is evaluated using data generated from a turbofan engine model. Simulation of engine output parameters over a series of flights with both nominal degradation and abrupt fault types has been conducted, and error within 1% for performance tracking and degradation prediction has been shown. This demonstrates the effectiveness of the developed technique in fault detection and degradation tracking in aircraft engines.