Aero-Engine Adaptive Model Re-Construction under Sensor Failure

The measured parameter deviations from nominal values can be used to predict the deterioration level of an aero-engine.A state variable model,which considered the deterioration factors,was developed.These deterioration factors were then treated as augmented states of the state variable model,thus they could be predicted by the Kalman filter based on sensed parameter deviations.If there was a sensor failure,the prediction results might not be appropriate.So a series of Kalman filters were designed as a diagnostic method to search the failed sensor.The adaptive model could be re-constructed once the failed sensor was found out.The immeasurable output parameters were trimmed by the predicted deteriorations.Adaptive performance of the re-constructed adaptive model were presented and compared with the model which had no sensor failures.The re-constructed adaptive model is shown to be able to well match the actual engine.