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http://dx.doi.org/10.5762/KAIS.2020.21.11.53

Flaw Evaluation of Bogie connected Part for Railway Vehicle Based on Convolutional Neural Network  

Kwon, Seok-Jin (Division of Advanced Railroad Vehicle, Korea Railroad Research Institute)
Kim, Min-Soo (Division of Advanced Railroad Vehicle, Korea Railroad Research Institute)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.21, no.11, 2020 , pp. 53-60 More about this Journal
Abstract
The bogies of railway vehicles are one of the most critical components for service. Fatigue defects in the bogie can be initiated for various reasons, such as material imperfection, welding defects, and unpredictable and excessive overloads during operation. To prevent the derailment of a railway vehicle, it is necessary to evaluate and detect the defect of a connection weldment between the car body and bogie accurately. The safety of the bogie weldment was checked using an ultrasonic test, and it is necessary to determine the occurrence of defects using a learning method. Recently, studies on deep learning have been performed to identify defects with a high recognition rate with respect to a fine and similar defect. In this paper, the databases of weldment specimens with artificial defects were constructed to detect the defect of a bogie weldment. The ultrasonic inspection using the wedge angle was performed to understand the detection ability of fatigue cracks. In addition, the convolutional neural network was applied to minimize human error during the inspection. The results showed that the defects of connection weldment between the car body and bogie could be classified with more than 99.98% accuracy using CNN, and the effectiveness can be verified in the case of an inspection.
Keywords
Convolutional Neural Network; Railway Bogie; Damage; Weldment; Defect;
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Times Cited By KSCI : 6  (Citation Analysis)
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