International Journal of Mechanical Engineering

Reliability and safety are important development directions of aero engines, and turbine blades are important hot end components of aero engines. Effective blade diagnosis methods can warn of abnormal state of aero engines in advance, reduce maintenance costs, and reduce the risk of major disasters. In this paper, based on HOG and SVM, a method of aero engine turbine blade anomaly detection is proposed. Firstly, thermal imaging images of aero engine blades at high temperature were obtained through experiments. Then, the high temperature thermal image of aero engine turbine blade is processed by gradient histogram through HOG, and the low-dimensional feature vector of the thermal image is obtained. And then the feature vector is input into SVM model to train the blade anomaly detection classifier. Finally, the test images are used to verify the effect of the anomaly detection classifier, and the recognition accuracy rate can reach 100% in the case of small samples. The method of aero engine blade anomaly detection based on HOG and SVM proposed in this paper provides a reference for aero engine fault diagnosis and intelligent turbine blade anomaly detection.

Thermal imaging; HOG; SVM; Aeroengine turbine blade