In order to realize better filtering accuracy and adaptability given different statistic characteristics of noises

an adaptive square root cubature Kalman filter (ASRCKF) was proposed for the turbofan engine gas path components fault diagnosis. In the ASRCKF algorithm

the mean and covariance of the engine nonlinear function were calculated by three-order cubature rule-based numerical integration method and used as a substitute of nonlinear model to avoid the parameters selection problem of nonlinear unscented Kalman filter. The latest information of measurement parameters in the recursion and filtering process was used to estimate and self-adjust the noise cross-covariance by removable window method

so as to realize higher filtering accuracy and adaptability of different noises. The comparison of simulation results indicates that the accuracy of the ASRCKF is improved by 40%-50% than the SRCKF

showing a better adaptability to different noises than other algorithms.