Uçak motorlarının baroskopik incelemesinde derin öğrenme kullanılarak hasar tespiti

Aircraft engine inspection is a key pillar of aviation safety by maintaining adequate performance standards to ensure the airworthiness of an engine. In addition, it is vital for asset value retention. Borescope inspection is currently the most widely used aircraft engine visual inspection method. However, borescope inspection is a time consuming, subjective, and complex process which heavily depends on the experience of the inspector as well as on their concentration level during inspection. On the other hand, cost saving of airlines and maintenance, repair, and overhaul (MRO) centers expose pressure and workload on inspectors. These make an automated system to support damage detection during borescope inspection necessary to avoid potential risks. Deep learning has found very wide application and has proven to be very successful during the last 10-15 years in the image recognition domain. In this research, we suggest a deep learning based automated damage detection framework from aircraft engine borescope inspection images. Faster R-CNN based deep learning model with Inception v2 feature extractor is utilized for the architecture. Due to the limited number of images, data augmentation and other overfitting methods are employed. The framework supports crack, burn, nick and dent damage types across all modules of turbofan engines. It is trained and validated with moderate to complex borescope images obtained from the field. The framework achieves 92.05% accuracy for nick or dent, 92.64% for crack and 81.14% for burn damage classes. Moreover, it achieves 88.61% average accuracy.