Nondestructive testing of train wheels using differential-type integrated Hall sensor matrixes embedded in train rails

Le, Minhhuy; Jun, Jongwoo; Kim, Jungmin; Lee, Jinyi

doi:10.1016/j.ndteint.2013.01.003

Cited by 16 publications

(5 citation statements)

References 7 publications

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“…This properties is also exhibite strengthen the Hermite-Gaussi [19,20,25]. pose, it is sufficient to know that mials which are the solution of a oblem [24].…”

Section: Hermite-gaussian Hypothesismentioning

confidence: 89%

Fast 2D crack profile reconstruction by image processing for Eddy-Current Testing

Betta

Ferrigno

Laracca

et al. 2015

2015 IEEE Metrology for Aerospace (MetroAeroSpace)

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Fast reconstruction of defects geometry is one of the main goal of Eddy Current Non-destructive testing. The information about the type of defects, their shapes, orientations and geometrical characteristics is of great importance in evaluating the impact of defects on structures' integrity and operation safety. From one point of view, the knowledge of the defect shape could give important information about the manufacturing, the use or the cause that have stressed the specimen and generated the defect, and from the other point of view the reconstruction of geometrical parameters of defect could be made easier if the shape of the defect is known. Although many inverse procedures have been developed for Eddy Current Testing, usually high-computational time and resources are requested, hampering the application of 3D reconstruction methods in on-line or in-situ inspections. In this paper a fast 2D defect profile reconstruction technique is presented based on image processing. The main idea of the proposed method is that images obtained from the maps of magnetic field on specimen with flaws, express a trend attributable to a class of known functions. In particular, the shape of the obtained image depends on both the type of defect that the type of measurement probe adopted. Then using a suitable reference image related to the response of the probe as a digital filter to process the acquired image through deconvolution or correlation operations, the shape of the flaw that has generated the image can be retrieved.

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“…This properties is also exhibite strengthen the Hermite-Gaussi [19,20,25]. pose, it is sufficient to know that mials which are the solution of a oblem [24].…”

Section: Hermite-gaussian Hypothesismentioning

confidence: 89%

Fast 2D crack profile reconstruction by image processing for Eddy-Current Testing

Betta

Ferrigno

Laracca

et al. 2015

2015 IEEE Metrology for Aerospace (MetroAeroSpace)

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“…X-ray computed tomography has been employed to address the reconstruction of rolling contact fatigue cracks in rails [ 6 ], and various ultrasonic testing methods have been applied in axle defect detection [ 7 , 8 ]. Given the complex structures of wheels and the multitude of components, there are a variety of detection methods [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Advancing Ultrasonic Defect Detection in High-Speed Wheels via UT-YOLO

Zhang,

Peng,

Tian

et al. 2024

Sensors

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In the context of defect detection in high-speed railway train wheels, particularly in ultrasonic-testing B-scan images characterized by their small size and complexity, the need for a robust solution is paramount. The proposed algorithm, UT-YOLO, was meticulously designed to address the specific challenges presented by these images. UT-YOLO enhances its learning capacity, accuracy in detecting small targets, and overall processing speed by adopting optimized convolutional layers, a special layer design, and an attention mechanism. This algorithm exhibits superior performance on high-speed railway wheel UT datasets, indicating its potential. Crucially, UT-YOLO meets real-time processing requirements, positioning it as a practical solution for the dynamic and high-speed environment of railway inspections. In experimental evaluations, UT-YOLO exhibited good performance in best recall, mAP@0.5 and mAP@0.5:0.95 increased by 37%, 36%, and 43%, respectively; and its speed also met the needs of real-time performance. Moreover, an ultrasonic defect detection data set based on real wheels was created, and this research has been applied in actual scenarios and has helped to greatly improve manual detection efficiency.

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“…In the railway field, non-destructive evaluation techniques, even non-conventional ones, are a fundamental tool for on-service inspection of components, such as wheels [1][2][3][4], axles [5][6][7][8], bogies [9,10], rails [11][12][13][14] and turnouts [15].…”

Section: Introductionmentioning

confidence: 99%