Least-squares health parameter identification techniques such as the Kalman filter have been massively used to solve the problem of turbine engine diagnosis. Indeed, such methods give a good estimate provided that the discrepancies between the model prediction and the measurements are zero-mean, white random variables. In turbine engine diagnosis, however, this assumption does not always hold due to the presence of biases in the model. This is especially true for transient operation. As a result, the estimated parameters tend to diverge from their actual values which strongly deteriorates the diagnosis. The purpose of this contribution is to present a Kalman filter diagnosis tool where the model biases are treated as an additional random measurement error. The new methodology is tested on simulated transient data representative of a current turbofan layout. While relatively simple to implement, the newly developed diagnosis tool exhibits a much better accuracy than the original Kalman filter in the presence of model biases.

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