Anomaly detection of four-engine aircraft based on Parameter differences

Abstract

Abstract In this paper, a real-time anomaly detection method combining GDN and Gaussian model is proposed for the real-time engine condition monitoring of four-engine aircraft. Firstly, the gas path parameters of the engine are selected and the difference of the gas path parameters is calculated to offset the influence of the external environment on the engine. Thirdly, the sliding time window method is used for real-time prediction, and the model prediction residuals are used for unsupervised anomaly detection. Finally, the Gaussian model is used to prune the anomalies detected by the GDN model to reduce the misjudgment of anomalies and improve the accuracy of anomaly detection. The experimental results show that the proposed method can detect 97.96% of the outliers in the test set and the accuracy reaches 89.47%, which is better than LSTM, AE, GDN, and Gaussian models alone.