Performance monitoring is a critical issue for gas turbine engine for improving the operation safety and reducing the maintenance cost. With regard to this, variants of Kalman-filters-based state estimation have been employed to detect gas turbine performance, but the classical centralized Kalman filters are subject to heavy computational effort and poor fault tolerance. A novel nonlinear fusion filter algorithm using information description with distributed architecture is proposed and applied to gas turbine performance monitoring. This methodology is developed from federated Kalman filter, and a bank of local extended information filters and one information mixer are combined with extended information fusion filter. The local state estimates and covariance calculated in parallel by the local extended information filters are integrated in the information mixer to yield a global state estimate. The global state estimate of nonlinear system is fed back to the local filters with weighted factor for next iteration. The aim of the proposed methodology is to reduce the computational efforts of state estimation and improve robustness to sensor faults in cases of gas turbine performance monitoring. The simulation results on a turbofan engine confirm the extended information fusion filter's effective capabilities in comparison to the general central ones.
Simultaneous local and global state estimation for robotic navigation
