Using deep learning improve the aerial engine nondestructive radiographic tests

Abstract

This paper aim use of deep convolutional neural networks (CNNs) with generative adversarial networks for aircraft engine X-ray cracks image classification and detection posed. On the basis of the CNNs approach requires large amounts of X-ray defect imagery data. Those data facilitate a cracks image segmentation and tracking on multiple defect of aircraft engine defection by edge detection feature extraction and classification process. The use of the deep CNNs approach deep learning model seeks to augment and improve existing automated nondestructive testing (NDT) diagnosis. Within the context of X-ray screening, limited numbers insufficient types of X-ray aircraft engine defect data samples can thus pose another problem in support vector machine (SVM) model accuracy. To overcome this issue, we employ a deep learning paradigm of generative adversarial network such that a pre-trained deep CNNs. We are primarily trained for aircraft engine defect X-ray image classification eight types where sufficient training data exists. This result are empirically show that deep learning net complex with the pre-tuned model features also more yield superior performance to human crafted features on object identification tasks. Overall the achieve result get more then 90% accuracy based on the DetectNet features model retrained with 8 types of composite material defect classifiers.