Depth quantification of rolling contact fatigue crack using skewness of eddy current pulsed thermography in stationary and scanning modes

Liu, Yi; Tian, Gui Yun; Gao, Bin; Lu, Xiaolong; Li, Haoran; Chen, Xiaotian; Zhang, Yuhua; Xiong, Longhui

doi:10.1016/j.ndteint.2022.102630

Cited by 40 publications

(8 citation statements)

References 29 publications

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“…Depth quantification has always been a challenge in the industrial field of NDT due to the potential jitter of the sensing structure during the inspection process. For addressing this challenge, our preliminary relevant studies are already under way [29]. For instance, in terms of group Ⅵ, the method of skewness (See the supplementary materials for more details) has been considered to evaluate their depths and has obtained the certain effect.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Rail Near-Surface Inspection of Multiphysical Coupled Electromagnetic and Thermography Sensing System

Gao

et al. 2023

IEEE Trans. Instrum. Meas.

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The effectiveness of railway fault inspection has remained challenging. Conventional techniques are still functionally limited and unable to meet the increasing demand of railway diagnosis. To mitigate the variety of rail fault detection problems, this paper proposes a dynamic railway inspection system based on multi-physical coupled electromagnetic and thermography sensing. It further shows the development and construction of a new inverted L-type magnet yoke abreast with volumetric coils array. The novel structure can not only significantly enhance the sensitivity and detectability of the region of interest (ROI), but also effectively detect the subsurface defects with the compensation of coils array due to the coupled electromagnetic field. Furthermore, the theoretical analysis of the coupled physical fields has been derived and proved to be consistent with the numerical simulation results. A rail test sample with various defects is carried out to verify the feasibility of the proposed system. Additionally, a metric learning post-processing algorithm has been conducted for distilling eddy current signals and thermograms to improve the accuracy of the detection results. On-site experimental and contrast results with various levels of performance validation have demonstrated that the integrated system is well suited for dynamic rail inspection on near-surface cracks at speed of 1 km/h.

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Section: Discussionmentioning

confidence: 99%

Dynamic Rail Near-Surface Inspection of Multiphysical Coupled Electromagnetic and Thermography Sensing System

Gao

et al. 2023

IEEE Trans. Instrum. Meas.

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“…Lately, there has been considerable research into eddy current pulsed thermography (ECPT) techniques for defect assessment, offering the advantage of rapid testing and clear visualization of subtle thermal variations. [ 51 ] In scanning mode, ECPT efficiently surveys extensive areas to detect irregularities swiftly. The primary strength of ECPT lies in its proficiency at detecting defects, yet it does not provide the same level of comprehensive deformation data as DIC.…”

Section: Discussionmentioning

confidence: 99%

Identification of Location and Geometry of Invisible Internal Defects in Structures using Deep Learning and Surface Deformation Field

Timilsina,

Jang,

et al. 2023

Advanced Intelligent Systems

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On‐site inspection of invisible subsurface defects in multiscale structural materials by conventional nondestructive testing (NDT) methods, such as X‐ray and ultrasound, requires complex sample preparation and data acquisition processes. Moreover, the inspected area is very small. Herein, a simple, inexpensive, and ultrasensitive NDT method for identifying and classifying the geometries of subsurface defects using commercial cameras, digital image correlation software, and object detection (OD) algorithms is developed. Three OD algorithms—Faster region‐based convolutional neural network (Faster R‐CNN), Mask R‐CNN, and you‐only‐look‐once (YOLO)v3—are evaluated for their ability to locate defects and identify defect geometries. Specifically, bounding boxes of two sizes (large and small) are applied to the regions of defect‐induced perturbations in strain tensors, which serve as virtual representatives of invisible subsurface defects. The performance of the proposed approach is validated on test datasets of known and unknown defect types. The experimental results confirm that the proposed approach can effectively utilize the surface deformation field information to accurately and reliably locate and identify subsurface defects. The method is nondestructive and low cost, enables real‐time detection, is robust against noise‐dominated deformation fields, and can be applied to various structural deformations. The method is therefore suitable for multiscale structural health monitoring and characterization of internal defects in materials.

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“…On other hand, crack sizing and imaging in NDT are also investigated using eddy current pulsed thermography (ECPT) and magnetic flux leakage (MFL) developed with both simulation and experimental studies. Liu 29 and Tian 30,31 proposed ECPT technology for characterization of Rolling Contact Fatigue (RCF) cracks and metallic materials using infrared (IR) camera based on induction heating and heat diffusion. Haoran 32 proposes a dynamic railway inspection system using multi‐physical coupled electromagnetic and thermography sensing.…”

Section: Introductionmentioning

confidence: 99%

Fast and efficient inversion methods for crack sizing using Bz magnetic signature

Barrarat,

Bensaid,

Rayane

et al. 2023

Int J Numerical Modelling

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Fast estimation is a critical feature of the proposed modeling approach as a “forward” fast solver that has a key role in solving the “inverse” problem involved in crack sizing, especially for real‐time applications. To this end, two different inversion methods have been proposed to estimate the crack depth. The first one uses an interpolation model of the 3D direct model simulation data. The second approach is based on artificial neural networks (ANN). In this article, the rotating uniform eddy current (RUEC) probe is used to detect the normal magnetic component Bz which is defined as the characteristic signal for reconstructing the crack length and depth concurrently. Using the first approach, The Bz characteristic 3D surface is presented, and this can be modeled by a fitted polynomial interpolation equation. Thus, the crack depth can be inverted using this equation referring to the experimental or simulated Bz signal and the crack length. The ANN is outlined to determine the crack depth based on the simulated Bz signature. The move from the first inversion method to the second was flexible and useful, in which the interpolation model is used as a defect signal generator for fast building of a large and efficient database for ANN training. Both of the proposed methods proved the objectivity and accuracy of the inversion results and offered more robust engineering support for automated NDT, reducing production time and increasing productivity.

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Depth quantification of rolling contact fatigue crack using skewness of eddy current pulsed thermography in stationary and scanning modes

Cited by 40 publications

References 29 publications

Dynamic Rail Near-Surface Inspection of Multiphysical Coupled Electromagnetic and Thermography Sensing System

Dynamic Rail Near-Surface Inspection of Multiphysical Coupled Electromagnetic and Thermography Sensing System

Identification of Location and Geometry of Invisible Internal Defects in Structures using Deep Learning and Surface Deformation Field

Fast and efficient inversion methods for crack sizing using Bz magnetic signature

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