Research and Rail Inspection

2009 ◽  
pp. 10-10-12
Keyword(s):  
Rail Inspection

Field Test Performance of Noncontact Ultrasonic Rail Inspection System

2017 ◽  
Vol 143 (5) ◽  
pp. 04017007 ◽  
Author(s):  
Stefano Mariani ◽  
Thompson Nguyen ◽  
Xuan Zhu ◽  
Francesco Lanza di Scalea
Keyword(s):  
Field Test ◽  
Test Performance ◽  
Inspection System ◽  
Rail Inspection

Application of LIDAR technology for rail surface monitoring and quality indexing

2017 ◽  
Vol 232 (5) ◽  
pp. 1398-1406 ◽  
Author(s):  
Masood Taheri Andani ◽  
Abdullah Mohammed ◽  
Ashish Jain ◽  
Mehdi Ahmadian
Keyword(s):  
Doppler Frequency ◽  
Light Detection And Ranging ◽  
Doppler Lidar ◽  
Frequency Range ◽  
Absorption Properties ◽  
Surface Conditions ◽  
Lidar Measurements ◽  
Rail Inspection ◽  
Surface Monitoring ◽  
Surface Figure

This paper investigates the application of Doppler Light Detection and Ranging (LIDAR) sensors for the assessment of the top of rail lubricity condition and layer material. Different top of rail conditions are distinguished by the system using a new pair of rail surface indices defined based on LIDAR measurements. These indices provide quantitative representations of the top of rail condition due to the fact that Doppler frequency range and spectral magnitude of a backscattered LIDAR beam are functions of the rail surface figure as well as the light absorption properties of the surface material. Laboratory tests are conducted to demonstrate the feasibility of the proposed top of rail indexing operation. The results indicate that LIDAR sensors are capable of detecting and distinguishing between different top of rail surface conditions. Instrumenting rail inspection vehicles with Doppler LIDAR systems reduces reliance on empirical top of rail lubricity and surface assessments (such as observing the sheen of the rail or tactilely sensing various residues on the rail), in favor of reliable and repeatable measurements.

A Novel Rail Inspection Robot and Fault Detection Method for the Coal Mine Hoisting System

2019 ◽  
Vol 11 (2) ◽  
pp. 110-121 ◽  
Author(s):  
Chaoquan Tang ◽  
Gongbo Zhou ◽  
Zhaoxing Gao ◽  
Xin Shu ◽  
Pengpeng Chen
Keyword(s):  
Fault Detection ◽  
Coal Mine ◽  
Detection Method ◽  
Inspection Robot ◽  
Rail Inspection ◽  
Hoisting System

scholarly journals Infrared and visible images registration with adaptable local-global feature integration for rail inspection

2017 ◽  
Vol 87 ◽  
pp. 31-39 ◽  
Author(s):  
Chaoqing Tang ◽  
Gui Yun Tian ◽  
Xiaotian Chen ◽  
Jianbo Wu ◽  
Kongjing Li ◽  
...  
Keyword(s):  
Feature Integration ◽  
Global Feature ◽  
Visible Images ◽  
Rail Inspection

scholarly journals PTZ Control System of Indoor Rail Inspection Robot Based on Neural Network Prediction Model

2017 ◽  
Vol 107 ◽  
pp. 206-211 ◽  
Author(s):  
Zhiyuan Liu ◽  
Xinyang Zhao ◽  
Jichao Sui ◽  
Hongli Wang ◽  
Yongcheng Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Prediction Model ◽  
Inspection Robot ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Rail Inspection

Seasonal Effect on the Optimization of Rail Defect Inspection Frequency

2013 ◽  
Author(s):  
Xiang Liu ◽  
C. Tyler Dick ◽  
Alexander Lovett ◽  
Mohd Rapik Saat ◽  
Christopher P. L. Barkan
Keyword(s):  
Seasonal Variation ◽  
Ultrasonic Inspection ◽  
Transportation Safety ◽  
Seasonal Effect ◽  
Infrastructure Management ◽  
Defect Inspection ◽  
Railroad Industry ◽  
Railway Infrastructure ◽  
Rail Inspection ◽  
Non Destructive

Broken rails are the most common cause of severe freight-train derailments on American railroads. Reducing the occurrence of broken-rail-caused derailments is an important safety objective for the railroad industry. The current practice is to periodically inspect rails using non-destructive technologies such as ultrasonic inspection. Determining the optimal rail defect inspection frequency is a critical decision in railway infrastructure management. There is a seasonal variation in the occurrence of broken rails that result in train derailments. This paper quantifies the effect of this seasonal variation on the risk-based optimization of rail inspection frequency. This research can be incorporated into a larger framework of broken rail risk management to improve railroad transportation safety.

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