Aero Engine Gas Path Performance Tracking Based on Multi-Sensor Asynchronous Integration Filtering Approach

Abstract

Kalman filter (KF) is a widely used technique to obtain health condition in aero engine health management, and each kind of measurement is commonly assumed to be collected and tackled simultaneously from one sensor in the KF for state tracking in previous studies. However, there are redundant sensor measurements of the same kind employed with different sampling and transmission rate, and it is hard to achieve state estimation using multiple sensors at one time, especially, in the advanced distributed architecture. For these purposes, a multi-sensor asynchronous fusion approach is proposed to track engine health state using distributed cubature information filter (CIF) in this paper. The sequential and distributed integration structures are designed for asynchronous computation, and the square-root cubature rule are combined with the CIF to complete state estimation. The global state estimate of distributed square-root CIF (DSCIF) is obtained from the fusion of local estimates using multi-scale sampling strategy. The performance comparisons of the examined methodologies are conducted to state estimation with asynchronous measurements in aero engine gas path health tracking applications. The results illustrate the superiority of the DSCIF with regards to estimation accuracy and computational efforts, and confirm our viewpoints in the asynchronous filtering estimation for the multi-sensor system.