Detection of Transverse Type-Defects in Rails using Ultrasonic Guided Waves

Teidj, Sara; Khamlichi, Abdellatif; Driouach, A.; Jacquelin, E.

doi:10.4203/ijrt.3.3.2

Cited by 4 publications

(3 citation statements)

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“…The excitation pulse can be generated by modulation of laser spot irradiation [6]. Here, the pulse duration was fixed at 450 T s ∆ µ = and a train of 20 sequential steps of the same duration was applied.…”

Section: Simulation Of Ultrasonic Wavesmentioning

confidence: 99%

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Reliability of rail transverse flaw detection by means of an embedded ultrasonic based device

2018

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The main causes of train derailment are related to rail defects such as head surface defects, horizontal defects, vertical longitudinal split defects, star cracks at colt holes and diagonal crack in web. The most adverse form of these defects consists of transverse flaws that are either opened or internal. These flaws are developed generally in a plane which is orthogonal to the rail direction. Detection of these defects is essential in order to improve security of rail roads and to prevent catastrophic failures. Most of the inspection techniques of rails relay on eddy currents, electromagnetic induction and ultrasounds. Unlike some conventional inspection tests based on contact excitation probes that roll on the rail head, this work deals with theoretical analysis of an integrated contact-less system based on ultrasound waves. Generation of these waves was performed by using a non-ablative laser source. A rotational laser vibrometer was used to perform reception of the echoes. Through finite element modelling of the rail system under specific ultrasound excitation of the rail head, detection of transverse rail flaws was studied. Reliability of defect detection was then assessed by considering various sources of perturbations.

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Section: Simulation Of Ultrasonic Wavesmentioning

confidence: 99%

“…Ultrasonic waves propagate in the rail structure under various dispersion patterns. When crossing an internal flaw, these waves produce echoes that can be sensed to determine some indications about the defect [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Reliability of rail transverse flaw detection by means of an embedded ultrasonic based device

2018

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“…As guided waves can propagate over relatively long distances, cover large areas and are sensitive to defects, they enable the long-range evaluation of rails from a single test point and have demonstrated great potential in nondestructive evaluation [2][3][4]. Many studies using guide waves focused mainly on the stock rail [5][6][7][8][9][10][11][12], and only a few studies [13][14][15][16][17] have focused on the switch rail. This study builds on our previous works that demonstrated the effectiveness of guided waves in structural health monitoring (SHM) of switch rails [14,15].…”

Section: Introductionmentioning

confidence: 99%

An efficient approach for guided wave structural monitoring of switch rails via deep convolutional neural network-based transfer learning

Liu¹,

Tang²,

Lv³

et al. 2022

Meas. Sci. Technol.

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Data-driven approaches in structural health monitoring have received increasing attention, especially advances in deep learning-based methods, which have further driven the development of data-driven damage detection. Due to the limited availability of guided wave samples and the imbalance between data classes, this study proposes a deep convolutional neural network-based transfer learning (DCTL) approach for the structure monitoring of switch rails using guided wave monitoring signals. A pretrained model based on Inception-ResNet-V2 was adopted and fine-tuned. Different methods for converting 1D signals into 2D images were investigated to find the optimal approach that meets practical monitoring requirements. Affine transformations were used for data augmentation to improve generalization ability and to avoid the overfitting of the training model. Two types of guided wave monitoring experiments on the foot and web of switch rails were conducted to evaluate the proposed method against different conventional methods in the field of switch rails. In addition, the DCTL method was investigated, with and without pretrained weights, along with different frozen layers. The classification results show that the proposed method can identify damage in challenging situations and outperforms conventional methods.

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