Optimizing Ultrasonic Rail Defect Inspection to Improve Transportation Safety and Efficiency

Liu, X.; Dick, C. Tyler; Saat, Mohd Rapik

doi:10.1061/9780784413586.074

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…Visual examination of the weld for visible defects by the welding personnel such as geometry is first test. Ultrasonic, Hardness, Slow Bends Test, Fracture Surface Examination, and Micro-and Macro Structure Examination are other tests for weld quality (Liu et al 2014). Ultrasonic Testing (UT) uses high frequency sound waves (typically 0.5 and 15 MHz) to conduct examinations and make measurements.…”

Section: Rail Weldingmentioning

confidence: 99%

See 1 more Smart Citation

Fatigue Life Analysis of Rail-Welds Using Linear Elastic Fracture Mechanics

Alamnie¹,

Endalemaw²

2019

Proceedings of International Structural Engineering and Construction

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The initiation and growth of fatigue cracking is mainly due to high stress concentration, heterogeneity and poor quality of weld. The detection and rectification of such weld defects are major concerns of rail network managers to reduce potential risk of rail breaks and derailments. To estimate the fatigue life of welded joints and to analyze the progress of fatigue cracks, a fracture mechanics-based analysis and fatigue models were developed using Finite Element Analysis. The initial flaw is obtained from a sample weld using ultrasonic flaw detecting machine test. Linear Elastic Fracture Mechanics (LEFM) approach based on the Paris law was applied to determine critical crack size and the number of cycles to failure using FRANC3D software. The inspection interval of rail welds before fracture (failure) was suggested based on reliability and life cycle analysis that correspond with minimum overall cost and frequency interval. It is recommended that fracture-based models in combination with reliability analyses can be a sustainable infrastructure decision-making algorithm.

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Section: Rail Weldingmentioning

confidence: 99%

“…Based on a model developed by AAR, the long-term cost of Rail-welds is given by Eq. (5) (Liu et al 2014).…”

Section: Optimal Inspection Intervalmentioning

confidence: 99%

Fatigue Life Analysis of Rail-Welds Using Linear Elastic Fracture Mechanics

Alamnie¹,

Endalemaw²

2019

Proceedings of International Structural Engineering and Construction

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“…One study suggests it could cost on average $300 per hour per vehicle. 8 Although FRA launched its own Automated Track Inspection Program (ATIP) for more than 30 years that is fully automated and can provide a full-scale rail geometry inspection, ATIP geometry vehicles are often cost-prohibitive 9,10 to short-lines (Class II and Class III railroads) that covers almost 1/3 of the entire U.S. rail network. The operations of these railroads are often constrained by their insufficient revenue 11 and lack of qualified employees, 12 as they are still heavily relying on the traditional, labor-intensive windshield or manual track inspections.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rail gage-based risk detection Using iPhone 12 pro

Ren

Dai

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part F:

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Federal Railroad Administration strictly regulates the inspection frequency of all track classes to ensure timely identification of rail defects including irregular gage which is a devastating rail geometry defect. Conventional rail inspection methods are both costly and labor-intensive, whereas existing novel technologies can be expensive and mostly focus on a specific inspection area, e.g. vertical alignment. iPhone 12 Pro was introduced to the public recently with a low-cost, low-resolution light detection and ranging (LiDAR) sensor that is purposed for better photography and virtual reality. Thanks to its portability and computational capacity, iPhone 12 Pro can potentially be used as a portable solution for irregular gage inspection, whose capacity and feasibility are unknown. This study first investigated the capability of the iPhone 12 Pro in calculating unloaded rail gages by its embedded LiDAR sensor. The results showed that uncalibrated raw gage values measured by the iPhone 12 Pro LiDAR sensor were systematically lower than the ground-truth values. The proposed method in this study then introduced logistic regression to calibrate the measured values through balancing the prediction performance and the efficiency, followed by validations using a Gaussian process classifier. The results show that the proposed method correctly identified all 39 high-risk locations with 227 false alarmed locations. The proposed method with the iPhone 12 Pro LiDAR sensor could potentially narrow down the possible “high-risk” gage sections and may result in a significant reduction in the field inspection workload by 48%.

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“…Due to the nature of the manual inspection, the results of inspecting missing components could be very low‐efficient and expensive. Based on Liu, Dick, and Saat (2014), except for the labor cost, the speed of inspection is around 15 to 20 mph and the average inspection cost per hour per vehicle is almost 300 dollars. Even with the manned‐inspection vehicles, the expected annual inspection cost is easily millions of dollars, let alone that a considerable amount of the missing track components is manually inspected by walking crew.…”

Section: Introductionmentioning

confidence: 99%

Automatic railroad track components inspection using real‐time instance segmentation

Guo

Qian

et al. 2020

Computer aided Civil Eng

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In the United States, to ensure railroad safety and keep its efficient operation, regular track inspections on track component defects are required by the Federal Railroad Administration (FRA). Various types of inspection equipment are applied, such as ground penetrating radar, laser, and LiDAR, but they are usually very expensive and require extensive training and rich experience to operate. To date, track inspections still rely heavily on manual inspections which are low‐efficiency, subjective, and not as accurate as desired, especially for missing and broken track components, such as spikes, clips, and tie plates. To address this issue, a real‐time pixel‐level rail components detection framework to inspect tracks timely and accurately is proposed in this study. The first public rail components image database, including rails, spikes, and clips, is built and released online. A real‐time pixel‐level detection framework with improved real‐time instance segmentation models is developed. The improved models leverage fast object detection and highly accurate instance segmentation. Backbones with more granular levels and receptive fields are implemented in the proposed models. Compared with the original YOLACT and Mask R‐CNN models, the proposed models are able to: (1) achieve 59.9 bbox mAP, and 63.6 mask mAP with the customized dataset, which are higher than the other models and (2) achieve a real‐time speed which is over 30 FPS processing a high‐resolution video (1,080 × 1,092) with a single GPU. The fast processing speed can quickly turn inspection videos into useful information to assist track maintenance. The railroad track components image dataset can be accessed at https://github.com/jonguo111/Rail_components_image_data

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Optimizing Ultrasonic Rail Defect Inspection to Improve Transportation Safety and Efficiency

Cited by 6 publications

References 10 publications

Fatigue Life Analysis of Rail-Welds Using Linear Elastic Fracture Mechanics

Fatigue Life Analysis of Rail-Welds Using Linear Elastic Fracture Mechanics

Rail gage-based risk detection Using iPhone 12 pro

Automatic railroad track components inspection using real‐time instance segmentation

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