Anomaly detection for health management of aircraft gas turbine engines

This paper presents a comparison of different pattern recognition algorithms to identify slow time scale anomalies for health management of aircraft gas turbine engines. A new tool of anomaly detection, based on Symbolic Dynamics and Information Theory, is compared with traditional pattern recognition tools of Principal Component Analysis (PCA) and Artificial Neural Network (ANN). Time series data of the observed variables on the fast time scale are analyzed at slow time scale epochs for early detection of anomalies. The time series data are obtained from a generic engine simulation model. Health monitoring of gas turbine engines based on these techniques is discussed.