Abstract
Eddy Current Pulsed Thermography is a crucial non-destructive testing technology which has a rapidly increasing range of applications for crack detection on metals. Although the unsupervised learning method has been widely adopted in thermal sequences processing, the research on supervised learning in crack detection remains unexplored. In this paper, we propose an end-to-end pattern, deep region learning structure to achieve precise crack detection and localization. The proposed structure integrates both time and spatial pattern mining for crack information with a deep region convolution neural network. Experiments on both artificial and natural cracks have shown attractive performance and verified the efficacy of the proposed structure.
Highlights
IntroductionStress concentration and surface cracks inevitably exist in mechanical infrastructure during the manufacturing and in-service processes
Non-destructive testing (NDT) plays an essential role in civil industry structures
According to the theory of the electromagnetic induction, the induced eddy current is excited in the conductor by an alternating
Summary
Stress concentration and surface cracks inevitably exist in mechanical infrastructure during the manufacturing and in-service processes. This leads to considerable hazards in industrial activities. MT is effective for crack detection on the surface and subsurface while its primary shortcomings are a complicated detecting procedure and pollution [2]. For a MT experiment, the surface of the sample requires pretreatment and the detection time is relatively long. Waste magnetic suspending liquid remains on the surface after the experiment, which causes chronic pollution. Cladding material covering the surface of the sample adversely affects the detection rate. This leads to ineffective inspection of micro-cracks
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