The characteristic atomic group of turbofan engine components was classified and exploited to the K-SVD(K-singular value decomposition) based on the sparse characteristics of gas path abrupt fault signals

then an improved K-SVD dictionary training algorithm was proposed and used for abrupt fault diagnosis. The compared results with EKF(extended Kalman filter) and UKF (unscented Kalman filter) showed that the improved K-SVD method was accurate for fault location

and the change of health parameters of no fault components was 0

which can improve the identification of fault components effectively and avoid misdiagnosis;the calculation time was basically the same as EKF method;under similar accuracy

the time consumption of this method was only 03% of UKF method

which can be adapted for engine gas path parameter tracking and abrupt faults diagnosis.