ABSTRACT Detection and diagnosis of material degradation are of a complex and challenging task since it is presently hand-operated by a human. Therefore, it leads to misinterpretation and avoids correct classification and diagnosis. In this paper, we develop a computer-assisted detection method of material failure by utilizing a deep learning approach. A deep convolutional neural network (CNN) model, combined with an image processing technique, e.g., adaptive histogram equalization, is trained to classify a real-world turbine tube degradation image data set, which is retrieved from a power generation company. The experimental result demonstrates the effectiveness of the proposed approach with predictive classification accuracy is up to 99.99% in comparison with a shallow machine learning algorithm, e.g., linear SVM. Furthermore, performance evaluation of a deep CNN with and without an above-mentioned image processing technique is exhibited and benchmarked. We successfully demonstrate a novel application in constructing a deep-structure neural network model for material degradation diagnosis, which is not available in the current literature. INDEX TERMS Material degradation, deep learning, creep damage, convolutional neural network, histogram equalization, boiler tube, high temperature.
For instance, work in REF presents a convolutional neural network (CNN) for detecting material degradation of turbine tubes in a power plant.
A Neural Network Model for Material Degradation Detection and Diagnosis Using Microscopic Images
IEEE Access
IEEE Access
Computer Science