Contracted Kalman filter estimation for turbofan engine gas-path health

Because of the limited number of sensors, the health estimation results for the gas-path in a turbo-fan engine are uncertain. Based on the contracted Kalman filter, a self tuning on-board model is proposed. By using a matrix transformation, we reduce the dimensions of the health parameter matrix. The weighted sum of the estimation bias and the variance of the contracted Kalman filter is employed as the object of optimization; and a subset of precise health parameters reflecting the engine performance in operations is obtained by using the adaptive genetic algorithm. The self tuning on-board model based on contracted Kalman filter is further proved theoretically. The simulation of a turbo-fan engine shows that the method of contracted Kalman filter with self tuning on-board model effectively estimates the health parameters when the sensor number is less than the health parameter number in the operation range.