A comparison of filtering approaches for aircraft engine health estimation

Different approaches for the estimation of the states of linear dynamic systems are commonly used, the most common being the Kalman filter. For nonlinear systems, variants of the Kalman filter are used. Some of these variants include the LKF (linearized Kalman filter), the EKF (extended Kalman filter), and the UKF (unscented Kalman filter). With the LKF and EKF, performance varies depending on how often Jacobians (partial derivative matrices) are updated. In other words, we see a tradeoff between computational effort and filtering performance. With the unscented Kalman filter, Jacobians are not calculated but computational effort is typically high due to the need for multiple simulations at each time step of the underlying dynamic system. Up to this point in time a number of filtering approaches have been used for aircraft turbofan engine health estimation, but a systematic comparison has not been published. This paper compares the estimation accuracy and computational effort of these filters for aircraft engine health estimation. We show in this paper that the EKF and UKF both outperform the LKF. The EKF computational effort is an order of magnitude higher than the LKF, and the UKF is another order of magnitude higher than the EKF. Overall we conclude that the EKF, with Jacobian calculations about every three flights, is the best choice for aircraft engine health estimation.