Aero engine gas path anomaly detection serves as a critical safeguard for the normal operation of aircraft. The aero engine gas path data comprises a substantial volume of normal data and a limited number of abnormal data and exhibits the characteristics of high dimensionality and strong coupling. Existing methods have difficulty in effectively extracting deep-level feature information from the original data, which leads to insufficient differentiation between normal and abnormal data, thereby seriously reducing the accuracy of anomaly detection. To address this issue, an unsupervised anomaly detection method based on the memory-adversarial autoencoder (Memory-AAE) is proposed. Firstly, the Memory-AAE model is constructed by integrating the memory network and adversarial training mechanism, which effectively enhances the capability to extract deep-level feature information. Then, a normal sample screening strategy is designed, which clusters the reconstruction errors of the original data by the K-means algorithm to obtain a refined normal sample dataset. Finally, the anomaly scoring mechanism based on Euclidean distance and mean absolute error is adopted to quantify the anomaly degree from both global and local dimensions, which effectively improves the detection capability of complex anomalies. In this paper, gas path anomaly detection experiments indicate that the precision, recall and F1 score of the proposed method reach 0.923, 0.915 and 0.919, respectively, and exhibit excellent robustness to noise, significantly outperforming other methods.
Liu et al. (2026) studied this question.