Automatic Defect Recognition and Localization for Aeroengine Turbine Blades Based on Deep Learning

Abstract

Radiographic testing is generally used in the quality management of aeroengine turbine blades. Traditional radiographic testing is critically dependent on artificially detecting professional inspectors. Thus, it sometimes tends to be error-prone and time-consuming. In this study, we gave an automatic defect detection method by combining radiographic testing with computer vision. A defect detection algorithm named DBFF-YOLOv4 was introduced for X-ray images of aeroengine turbine blades by employing two backbones to extract hierarchical defect features. In addition, a new concatenation form containing all feature maps was developed which play an important role in the present defect detection framework. Finally, a defect detection and recognition system was established for testing and output of complete turbine blade X-ray images. Meanwhile, nine cropping cycles for one defect, flipping, brightness increasing and decreasing were applied for expansion of training samples and data augmentation. The results found that this defect detection system can obtain a recall rate of 91.87%, a precision rate of 96.7%, and a false detection rate of 7% within the score threshold of 0.5. It was proven that cropping nine times and data augmentation are extremely helpful in improving detection accuracy. This study provides a new way of automatic radiographic testing for turbine blades.

Keywords

• aeroengine turbine blades
• X-ray images
• radiographic testing
• defect detection
• deep learning
• quality management