Prediction of the coexistence of rail head check initiation and wear growth

2018 ◽  
Vol 112 ◽  
pp. 289-300 ◽  
Author(s):  
Yu Zhou ◽  
Yanbin Han ◽  
Dongsheng Mu ◽  
Congcong Zhang ◽  
Xuwei Huang
Keyword(s):  
Rail Head

Оценка реальной чувствительности приемочного ультразвукового контроля рельсов

2020 ◽  
pp. 17-27
Author(s):  
А.А. Шелухин
Keyword(s):  
Ultrasonic Pulse ◽  
Piezoelectric Transducers ◽  
Acoustic Path ◽  
Metallic Inclusion ◽  
Inclusion Type ◽  
The Real ◽  
Rail Head ◽  
Pulse Echo

In this article, the analysis of the acoustic path during the ultrasonic pulse echo testing of the rail head in production is carried out. The influence of the parameters of the applied piezoelectric transducers on the distribution of sensitivity for the sounding scheme used in the existing installations is estimated and the real sensitivity of detecting defects of the «non-metallic inclusion» type is estimated.

scholarly journals UAVs in rail damage image diagnostics supported by deep-learning networks

2021 ◽  
Vol 11 (1) ◽  
pp. 339-348
Author(s):  
Piotr Bojarczak ◽  
Piotr Lesiak
Keyword(s):  
Deep Learning ◽  
Head Width ◽  
Learning Networks ◽  
Image Brightness ◽  
Learning Network ◽  
Rail Head ◽  
Image Recording ◽  
Python Language ◽  
Deep Learning Network ◽  
Efficiency Rate

Abstract The article uses images from Unmanned Aerial Vehicles (UAVs) for rail diagnostics. The main advantage of such a solution compared to traditional surveys performed with measuring vehicles is the elimination of decreased train traffic. The authors, in the study, limited themselves to the diagnosis of hazardous split defects in rails. An algorithm has been proposed to detect them with an efficiency rate of about 81% for defects not less than 6.9% of the rail head width. It uses the FCN-8 deep-learning network, implemented in the Tensorflow environment, to extract the rail head by image segmentation. Using this type of network for segmentation increases the resistance of the algorithm to changes in the recorded rail image brightness. This is of fundamental importance in the case of variable conditions for image recording by UAVs. The detection of these defects in the rail head is performed using an algorithm in the Python language and the OpenCV library. To locate the defect, it uses the contour of a separate rail head together with a rectangle circumscribed around it. The use of UAVs together with artificial intelligence to detect split defects is an important element of novelty presented in this work.

The Effect of Deformation on Mechanical Properties and Shakedown on Railroad Wheels

2014 ◽  
Author(s):  
Steven L. Dedmon ◽  
Takashi Fujimura ◽  
Daniel Stone
Keyword(s):  
Mechanical Properties ◽  
Rolling Contact ◽  
Effect Of Temperature ◽  
Plastic Deformations ◽  
Rail Head ◽  
Class D ◽  
Shakedown Theory ◽  
Freight Car ◽  
Class C ◽  
Railroad Wheels

Plastic deformations alter the mechanical properties of many metals and alloys. Class C and Class D wheel steels such as are used in North American freight car service are particularly affected by plastic deformations occurring during rolling contact between the wheel tread and rail head. This investigation determines the effect plastic deformations have on the mechanical properties of Class C and D wheel steels and how those changes could relate to shakedown theory. The effect of temperature is also discussed.

scholarly journals Application of the wavelet and neural technologies for processing of signals obtained during railway tracks diagnostics by the magnetic flux leakage method

2017 ◽  
Vol 66 (4) ◽  
pp. 209-216
Author(s):  
Vitalij Nichoga ◽  
Liubomyr Vashchyshyn
Keyword(s):  
Magnetic Flux ◽  
Transverse Crack ◽  
Magnetic Flux Leakage ◽  
Rail Head ◽  
Railway Tracks ◽  
Further Development ◽  
Flux Leakage

In this article, the approach for detecting a transverse crack in the rail head via ANN with CWT and application created on its basis are presented. The ways of further development of the ANN for improving its work accuracy and the possibility of identification of other types of defects are also presented. Keywords: defect, transverse crack, CWT, ANN

Research on Quenching Equipment of Rail Head

2010 ◽  
Vol 145 ◽  
pp. 313-316 ◽  
Author(s):  
Hao Kang ◽  
Yong Hong Wang ◽  
Di Wu ◽  
Xian Ming Zhao ◽  
Yong Ming Wang
Keyword(s):  
Cooling Rate ◽  
Cross Section ◽  
Current Problem ◽  
Natural Aging ◽  
Al Alloy ◽  
Rail Head ◽  
Heating Device

As to current problem of straightness defects and uneven quenching thickness of rail head, heating device with several induction loops are used, and through a three step strategy to realize simultaneously heating to head and bottom of rail. In air jetting phase, spray nozzles are replaced by Al alloy plates(20mm thick) with holes, adjusting to cooling strengthen can be realized by changing cross-section of holes, cooling rate is 2~4°C/sec. After the air jetting phase, there is a waterfog jetting phase, which makes 200°C temperature gap between head and bottom of rail, to ensure quenched rail has good straightness after 48h natural aging.

Fatigue Analysis of Rail-Head-to-Web Fillet at Bolted Rail Joint Under Various Impact Wheel Load Factors and Support Configurations

2016 ◽  
Author(s):  
Kaijun Zhu ◽  
J. Riley Edwards ◽  
Yu Qian ◽  
Bassem O. Andrawes
Keyword(s):  
Fatigue Life ◽  
Fe Model ◽  
Wheel Load ◽  
Transit Systems ◽  
University Of Illinois ◽  
Rail Head ◽  
Load Factors ◽  
Continuous Welded Rail ◽  
The University ◽  
Better Than

As one of the weakest locations in the track superstructure, the rail joint encounters different types of defects and failures, including rail bolt-hole cracking, rail head-web cracking or separation, broken or missing bolts, and joint bar cracking. The defects and failures are mainly initiated by the discontinuities of both geometric and mechanical properties due to the rail joint, and the high impact loads induced by the discontinuities. Continuous welded rail (CWR) overcomes most disadvantages of the rail joints. However, a large number of rail joints still exist in North American Railroads for a variety of reasons, and bolted joints are especially prevalent in early-built rail transit systems. Cracks are often found to initiate in the area of the first bolt-hole and rail-head-to-web fillet (upper fillet) at the rail end among bolted rail joints, which might cause further defects, such as rail breaks or loss of rail running surface. Previous research conducted at the University of Illinois at Urbana-Champaign (UIUC) has established an elastic static Finite Element (FE) model to study the stress distribution of the bolted rail joint with particular emphasis on rail end bolt-hole and upper fillet areas. Based on the stress calculated from the FE models, this paper focuses on the fatigue performance of upper fillet under different impact wheel load factors and crosstie support configurations. Preliminary results show that the estimated fatigue life of rail end upper fillet decreases as impact factor increases, and that a supported joint performs better than a suspended joint on upper fillet fatigue life.

Characterize Rail Crack Pattern Through Image Analysis

2020 ◽  
Author(s):  
Ryan DeVine ◽  
Yu Qian ◽  
Yi Wang ◽  
Shaofeng Wang ◽  
Dimitris Rizos
Keyword(s):  
Image Analysis ◽  
Growth Pattern ◽  
Crack Detection ◽  
Fuel Efficiency ◽  
Vertical Direction ◽  
Safety Requirements ◽  
Detection Techniques ◽  
Rail Head ◽  
Length Direction ◽  
Filtering Technique

Abstract Railway provides more than 40% of the freight ton-miles moved in the U.S. each year, surpassing all other modes of transportation. In addition to moving more tonnage farther than other modes, trains have better fuel efficiency than trucks and airplanes due to the low friction between the wheels and the rails. With traffic accumulation, rails will degrade which may lead to different types of defects, including but not limited to spalling, separation, crack, and corrugation. Rail head fissures or surface crack is often associated with rolling fatigue and must be addressed through grinding or other maintenance activities to restore the smooth-running surface. This ensures the riding conforms to operational safety requirements. The growth pattern of rail surface cracks has not been thoroughly understood or well-quantified yet due to the difficulties of rail crack inspection and insufficient data. This paper presents a study that uses image analysis techniques to detect and quantify cracks in images of rail segments that were taken in the field. Various crack detection techniques were tested and compared with visual inspection, including thresholding, edge detection, and bottom-hat filtering. The crack length, direction, and curvature were also quantified with each approach. Cracks were found to grow not perpendicular to the rail head, but with a certain angle from the vertical direction and relatively evenly distributed along the rail. The bottom-hat filtering technique was found to be the best in terms of accuracy among the methods tested in this study. The results from the study fill the gap of the literature by quantitatively characterizing the rail crack growth pattern and helping to identify possible approaches for future autonomous crack detection.

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