Guided wave–based rail flaw detection technologies: state-of-the-art review

Ge, Hao; Huat, David Chua Kim; Koh, Cheng-Gee; Dai, Gonglian; Yu, Yang

doi:10.1177/14759217211013110

Cited by 43 publications

(25 citation statements)

References 159 publications

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“…Many studies have utilized direct wave to obtain clear responses by generating and extracting pure mode of ultrasonic guided waves [ 53 , 54 ]. However, methods for exciting the ideal mode and minimizing dispersion in railway tracks need to be further studied [ 55 ].…”

Section: Methodsmentioning

confidence: 99%

Diffuse Ultrasonic Wave-Based Damage Detection of Railway Tracks Using PZT/FBG Hybrid Sensing System

Sun

Guo

Yuan

et al. 2022

Sensors

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Damage detection of railway tracks is vital to ensure normal operation and safety of the rail transit system. Piezoelectric sensors, which are widely utilized to receive ultrasonic wave, may be disturbed in the railway system due to strong electromagnetic interference (EMI). In this work, a hybrid ultrasonic sensing system is proposed and validated by utilizing a lead-zirconate-titanate (PZT) actuator and a fiber Bragg grating (FBG) sensor to evaluate damage conditions of the railway tracks. The conventional ultrasonic guided wave-based method utilizing direct wave to detect damages is limited by the complex data analysis procedure and low sensitivity to incipient damage. Diffuse ultrasonic wave (DUW), referring to later arrival wave packets, is chosen in this study to evaluate structural conditions of railway tracks due to its high sensitivity, wider sensing range, and easy implementation. Damages with different sizes and locations are introduced on the railway track to validate the sensitivity and sensing range of the proposed method. Two damage indices are defined from the perspective of energy attenuation and waveform distortion. The experimental results demonstrate that the DUW signals received by the hybrid sensing system could be used for damage detection of the railway tracks and the waveform-distortion-based index is more efficient than the energy-based index.

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Section: Methodsmentioning

confidence: 99%

Diffuse Ultrasonic Wave-Based Damage Detection of Railway Tracks Using PZT/FBG Hybrid Sensing System

Sun

Guo

Yuan

et al. 2022

Sensors

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“…However, there are still some issues that need to be resolved, as follows: (1) Some defects are distributed in the edge part of the image, and the gray value of the defect is the same as the gray value of the edge, so it is difficult to be detected. (2) The scale of defects varies greatly, and the fusion method of fixed weights will lead to the loss of the underlying detail features, which will make the detection effect of small target defects worse.…”

Section: Yolo Target Detection Networkmentioning

confidence: 99%

“…Therefore, timely detection of the health status of the rail surface is essential for preserving the security of the train. In traditional rail surface defect detection, the inspection methods are mostly ultrasonic [2], eddy current [3], and magnetic particle [4] methods. Although these methods can detect rail surface defects, they require much time.…”

Section: Introductionmentioning

confidence: 99%

Detection of Surface Defects on Railway Tracks Based on Deep Learning

Wang

Zhu

et al. 2022

IEEE Access

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The detection of rail surface defects is very important in railway transportation. However, the edge defects on both sides of the rail and the multi-scale variation between different types of defects both pose challenges to the detection of rail surface defects. In order to solve the above problems, this paper proposes a novel rail surface defect detection network, YOLOv5s-VF. First, we design a sharpening functional attention mechanism (V-CBAM) that contains two key components: adaptive channel attention (F-CAM) and sharpened spatial attention (SSA). In F-CAM, we use one-dimensional convolution with adaptive convolution kernels for cross-channel connections, which reduces the number of parameters of the attention mechanism without affecting its performance. In SSA, we design a sharpening filter suitable for spatial attention, which is used to enhance the attention to the edge position defects of railway tracks and enhance the detection effect of the network on edge defects. Second, we construct a microscale adaptive spatial feature fusion (M-ASFF), which adds a high-resolution feature extraction layer to enhance the details of the underlying features of tiny defects. At the same time, in order to prevent the loss of detailed information and the excessive increase of the parameters of the model, the low-resolution feature layer is removed. Combined with adaptive spatial feature fusion, it can prevent the semantic conflict caused by the fusion of features at different scales. Finally, given the lack of labeled public rail surface defect datasets, this paper is based on the collection of real rail images and manually labels defects to train an object detection network and open source it. The experimental results show that YOLOv5s-VF outperforms the existing rail surface defect detection methods with a detection accuracy of 93.5% and a detection speed of 114.9 fps.

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“…Also, it can monitor the abnormal reaction of the wheels of trains to detect and repair the track defects and abnormal wheel sound in time. Up to now, the existing methods to monitor the track defects, are mainly implemented by ultrasonic detection [1], eddy current detection [2], acceleration measurement [3], and video images [4,5]. These single-point electronic sensors have insurmountable limitations of nonlinearity, zero-point drift, and low tolerance to strong electromagnetic environments along the transmission lines.…”

Section: Introductionmentioning

confidence: 99%

Detection of railway wheel and rail diseases based on distributed optical fiber acoustic sensing

Tang¹,

Peng²,

Li³

et al. 2022

Advanced Sensor Systems and Applications XII

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This research proposes a real-time detection method for the health status of heavy-haul railways based on distributed fiber optic acoustic sensing (DAS). The DAS system detects the wheel-rail acoustic signals using the communication optical cable along the heavy-haul railway, multiple features are mixed to construct an eigenvector and a classifier to realize the typical disaster identification of the heavy-haul railway. The experimental results show that this system can realize the identification and classification of typical track diseases such as rolling contact fatigue (RCF), corrugation, unsupported sleepers on the railway, the achieved average identification rate of disease events is as high as 97.3%, and the identification time of a single event sample is 1ms. This work can achieve real-time detection of track diseases, which can be used as an important basis for workers to maintain and repair. In addition, the DAS system has also successfully monitored the train's running speed and wheel anomalies status information. This work provides a long-term online monitoring method for rail safety operation and maintenance of railway transportation and monitoring of train running status, and does not require any additional sensor arrangement.

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Guided wave–based rail flaw detection technologies: state-of-the-art review

Cited by 43 publications

References 159 publications

Diffuse Ultrasonic Wave-Based Damage Detection of Railway Tracks Using PZT/FBG Hybrid Sensing System

Diffuse Ultrasonic Wave-Based Damage Detection of Railway Tracks Using PZT/FBG Hybrid Sensing System

Detection of Surface Defects on Railway Tracks Based on Deep Learning

Detection of railway wheel and rail diseases based on distributed optical fiber acoustic sensing

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