A Neural Network Model for Material Degradation Detection and Diagnosis Using Microscopic Images

IEEE Access

卷:7

Seungchul Lee¹  Bayu Adhi Tama¹  Hyunsuk Huh¹  Woosung Choi²  Gyusang Park² 

[1] Department of Mechanical Engineering, POSTECH, Pohang, South Korea;

[2] Power Generation Laboratory, KEPCO Research Institute, Daejeon, South Korea;

关键词: Material degradation;    deep learning;    creep damage;    convolutional neural network;    histogram equalization;    boiler tube;   

DOI  :  10.1109/ACCESS.2019.2927162

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown