Efficient Fatigue Crack Detection in Steel Structures Using Infrared Thermographic Testing: A Convolutional Neural Network Approach

This paper discusses efficient maintenance methods for steel structures built over 50 years ago. One of the challenges faced in the maintenance efforts of a steel mill, in which the authors are involved, is the efficient detection of fatigue cracks that occur in frequently used overhead cranes and their associated equipment. In particular, screening methods are needed for runway girders supporting the crane’s running rails, which require time-consuming inspections for fatigue cracks. Therefore, this paper considers an efficient screening method for fatigue cracks that occur under the triangular ribs of runway girders. While inspection methods using thermoelastic effect have been proposed in the past, they have limitations in detecting crack shapes and measuring crack lengths. Therefore, a new method for detecting fatigue cracks using infrared radiation is proposed. Deep learning techniques are also considered to improve the efficiency of detecting fatigue cracks from the captured thermal images. The proposed method was evaluated on the runway girders of a steel mill, demonstrating its ability to detect fatigue cracks of 10cm or more.