Diagnosis of gas path health states on a real flight data by convolutional neural network and dual-tree complex wavelet

As the core component of aircraft, the reliability and availability of aero-engines directly affect the entire cruise process of aircraft. Implementing health monitoring in the gas path of an aero-engine can ensure engine reliability and availability to a greater extent. It can also enable the move from traditional time-scheduled maintenance to condition-based maintenance and reduce life cycle costs. The existing models were mainly concentrated on simulation data and traditional machine learning methods. This paper provides a new pathway to classify the states of engine gas path performance by combining the convolutional neural network (CNN) with the dual-tree complex wavelet (DTCW) method. A real flight data accompanied by maintenance records collected from the airline was adopted to construct a data set. The data pre-processing and stable points extraction were carried out, and the combined CNN and DTCW method was developed to diagnose the healthy states of the gas path. The final accuracy can achieve higher than 94%. The diagnosis results indicate that the deep learning methods hold particular promise for aero-engine health monitoring.