Aero-engine anomaly detection is of great significance to accurately understand aircraft health status,support maintenance decision and ensure flight safety.A dynamic threshold anomaly detection method based on slow feature analysis and local outlier factor was proposed for the long-term degradation behavior of aero-engine gas-path components.The advantages of slow feature analysis were fully utilized to extract the effective features of slow degradation of gas path parameters with time.The local outlier factors of the feature space samples were calculated to construct the monitoring statistics and quantitatively characterize the health state of the engine.Considering the poor adaptability of the fixed threshold to the time-varying characteristics of the gas path state,the adaptive window adjustment strategy based on the local distribution difference was used to set the dynamic threshold to effectively reduce the false alarm caused by small fluctuations of the gas path parameters.The test results show that the proposed method can identify the abnormal points in advance and reduce the occurrence of false alarms effectively.