Aero-Engine Blade Defect Detection: A Systematic Review of Deep Learning Models

Abstract

Aero-engine blade defect detection is a crucial task in ensuring the safety and reliability of aircraft. The visual inspection of aero-engine blades is a complex process that requires extensive knowledge and experience. This paper presents a systematic literature review (SLR) of deep learning models for detecting defects in aero-engine blades. The review considers 13 primary studies, including methods and conceptual works. This is the first systematic review of deep learning models for aero-engine blade defect detection. The findings of this review demonstrate the potential of deep learning in detecting blade defects and improving the accuracy and efficiency of visual inspection. However, there is a need for more research on integrating deep learning models into practical applications and developing robust and reliable systems for defect detection. This review framework provides a comprehensive methodology for selecting and evaluating relevant studies, which researchers can use for future investigations in this area. These results should encourage further work on deep learning techniques, system integration, and testing and validation for defect detection of aero-engine blades.