On-Line Measurement Validation and Performance Monitoring Using Robust Kalman Filtering Techniques

In an on-line engine performance monitoring, two tasks must be simultaneously
carried out: an on-line engine health parameter estimation and an on-line engine
state variables estimation. Estimation of the state variables constitutes a general
application of the Extended Kalman Filter theory, while the health parameter estimation
is a classical recurrent regression problem. Recent advances show that
both problems can be solved by two Kalman filters working jointly. Such filters are
usually named Dual Kalman Filters.
The present contribution aims at using a modified dual Kalman filter to provide
robustness. This procedure should be able to cope with as much as 20 to 30% of
faulty data. The resulting on-line validation method has been applied to a turbofan
model developed in the frame of the OBIDICOTE 1 project. Several tests have been
carried out to check the performance monitoring capability and the robustness that
can be achieved