Aeroengine performance monitoring and fault diagnosis based on transient measurements

Aeroengine components performances deteriorate due to wearing, foreign object ingestion damage or other factors. The engine monitoring and fault diagnostics systems are developed to trace the components health condition and to help make the maintenance decision in order to avoid severe damages. Based on transient gas path measurements, the engine monitoring and diagnostics are carried out through the components health parameters estimation. A novel reduced sigma point square root unscented Kalman filter (SR-UKF) was proposed by combining the SR-UKF with a simplex spherical sigma point selection strategy, which guarantees high accuracy and decreases the computational cost greatly. Some typical gradual deteriorations and abrupt component faults diagnosis are carried out as test cases on a real-time two-spool turbofan engine model. Digital simulation results prove the effectiveness of the proposed method.