The key to failure prevention for aero-engine lies in performance prediction and the exhaust gas temperature margin(EGTM)is used as the most important degradation parameter to obtain the operating performance of the aero-engine.Because of the complex environment interference,EGTM always has strong randomness,and the state space based degradation model can identify the noisy observation from the true degradation state,which is more close to the actual situations.Therefore,a state space model based on EGTM is established to describe the degradation path and predict the remaining useful life(RUL).As one of the most effective methods for both linear state estimation and parameter estimation,Kalman filter(KF)is applied.Firstly,with EGTM degradation data,state space model approach is used to set up a state space model for aero-engine.Secondly,RUL of aero-engine is analyzed,and expected RUL and distribution of RUL are determined.Finally,the sate space model and KF algorithm are applied to an example of CFM-56aero-engine.The expected RUL is predicted,and corresponding probability density distribution(PDF)and cumulative distribution function(CDF)are given.The result indicates that the accuracy of RUL prediction reaches 7.76%ahead 580 flight cycles(FC),which is more accurate than linear regression,and therefore shows the validity and rationality of the proposed method. The key to failure prevention for aero-engine lies in performance prediction and the exhaust gas temperature margin (EGTM) is used as the most important degradation parameter to obtain the operating performance of the aero-engine. Failure of the complex environment interference, EGTM always has strong randomness, and the state space based degradation model can identify the noisy observation from the true degradation state, which is more close to the actual situations. Before, a state space model based on EGTM is established to describe the degradation path and predict the remaining useful life (RUL). As one of the most effective methods for both linear state estimation and parameter estimation, Kalman filter (KF) is applied. Firstly with EGTM degradation data, state space model approach is used to set up a state space model for aero-engine. Secondarily, RUL of aero-engine is analyzed, and expected RUL and distribution of RUL are determined. Finally, the sate space model and KF algorithm are applied to an example of CFM-56 aero-engine. The expected RUL is predicted, and corresponding probability density distribution (PDF) and cumulative distribution function (CDF) are given. The result indicates that the accuracy of RUL prediction reaches 7.76% ahead of 580 flight cycles (FC ), which is more accurate than linear regression, and therefore shows the validity and rationality of the proposed method.