A novel degradation estimation of lifecycle health monitoring for aircraft engine gas path rotational components

Abstract A novel degradation estimation method is proposed to draw out the engine performance deterioration rules of multiple rotational-component parameters simultaneous variations from available sensors. The proposed methodology consists of the extended Kalman filtering (EKF) and an enhanced optimization strategy. Gas-path parameter analysis on the impact of components performance is implemented to determine the key degradation feature. The unmeasured feature is randomly generated to estimate performance drifts of rotational components by the EKF group and steady model. The achieved performance parameters stream to the engine dynamic model to yield measurements’ estimated series, which are utilized to construct the fitness function. Thus, the optimization gains the unmeasured feature and the deteriorations of engine performance. This paper reaches simultaneous performance drifts of all components under the limited sensors. Simulation results show the suggested approach provides the precise unmeasured feature, and quantify variations in the simultaneous drift of engine performance during its usage.