Automatic Detection for Defects of Railroad Track Surface

Guo, Long; Zhang, Jun; Chen, Zhe; Sun, Liang; Ge, Jia; Lü, Kun Lin; Dai, Guang Yu

doi:10.4028/www.scientific.net/amm.278-280.856

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Other non-destructive testing (NDT) techniques adopted to evaluate the rail defects include the vision-based techniques, ultrasound measurements, eddy current testing (ECT) systems, accelerometers, etc. [12] The vision-based technique is one common method in current rail-defect detection studies [13,14]. The existing methods can be divided into two groups, namely, traditional image processing and image processing using machine learning.…”

Section: Introductionmentioning

confidence: 99%

Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning

et al. 2023

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Switches and crossings (S&Cs) are also known as turnouts or railway points. They are important assets in railway infrastructures and a defect in such a critical asset might lead to a long delay for the railway network and decrease the quality of service. A squat is a common rail head defect for S&Cs and needs to be detected and monitored as early as possible to avoid costly emergent maintenance activities and enhance both the reliability and availability of the railway system. Squats on the switchblade could even potentially cause the blade to break and cause a derailment. This study presented a method to collect and process vibration data at the point machine with accelerometers on three axes to extract useful features. The two most important features, the number of peaks and the total power, were found. Three different unsupervised machine learning algorithms were applied to cluster the data. The results showed that the presented method could provide promising features. The k-means and the agglomerative hierarchical clustering methods are suitable for this data set. The density-based spatial clustering of applications with noise (DBSCAN) encounters some challenges.

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Section: Introductionmentioning

confidence: 99%

Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning

et al. 2023

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“…These weaknesses emphasize the need for more advanced and efficient methods of rail and S&C inspection to improve safety and reliability. The techniques include visual inspection [7][8][9], ultrasonic testing [10][11][12], use of the eddy current testing (ECT) system [13,14], and measuring vibrations [15][16][17]. However, all these current methods have limitations of one kind or another.…”

Section: Introductionmentioning

confidence: 99%

Wayside Railway Switch and Crossing Monitoring Using Isolation Forest Anomaly Scores

Zuo,

Chandran,

Odelius

et al. 2023

Sustainability

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Railway switch and crossing (S&C) systems have complicated moving structures compared with regular rail. They require multiple components that vary in complexity. The complexity of railway S&C, together with the fact that they are discontinuous points of the system, makes them vulnerable to defects such as squats. A squat on the switching rail could potentially cause rail breakage and lead to catastrophic results, such as derailment. In this study, a method based on anomaly scoring was investigated to estimate the status of an S&C system with respect to squat defects. The proposed method was tested in a real environment under controlled measurement sequences. The results show that the methods can differ between an S&C with squats and another one without them.

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“…Machine-visions method is the pop method for the detection of railroad track surface defects, it includes two steps: high-speed camera is used to get pictures of railroad track surface; the pictures are detected with the digital image processing to determine the position of defects. In china, several institutions or university obtain some good results, for example, Xin Guan [4] etc clip the image with fixed width to extract the real track image accurately, and then locate the defects by the edge detection method with adaptive threshold, at last divide the defects into cracks and scars with the neural network identification method; Long Guo etc [6] extract track surface defects with Improved Sobel operator and filter method. These methods are useful for post image processing.…”

Section: Introductionmentioning

confidence: 99%

Online Real-Time Detection Method for Defects of Railroad Track Surface

Dai¹,

Zhang²,

Lü³

et al. 2013

AMM

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In the field of detecting for defects of railroad track surface, computer vision method for detection has been widely used. An online real-time image processing system for defects of railroad track surface is introduced in the paper. Results show that: if the number of pixels in each railroad track surface image is 512×240, the train speed can be 50 Km/h while the image processing system can real-time detect the railway track surface defects, the number of pixels in in each railroad track surface image is 320×250, the train speed can be 100 Km/h while the image processing system can real-time detect the railway track surface defects.

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Automatic Detection for Defects of Railroad Track Surface

Cited by 4 publications

References 7 publications

Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning

Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning

Wayside Railway Switch and Crossing Monitoring Using Isolation Forest Anomaly Scores

Online Real-Time Detection Method for Defects of Railroad Track Surface

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