A method for detecting the wheel rail attack angle based on laser line detection

Zengqiang Ma; Zibin Song; Yongsheng Wang

doi:10.3969/j.issn.1003-501X.2017.08.009

Article navigation > Opto-Electronic Engineering > 2017 Vol. 44 > No. 8 > 818-825

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Zengqiang Ma, Zibin Song, Yongsheng Wang. A method for detecting the wheel rail attack angle based on laser line detection[J]. Opto-Electronic Engineering, 2017, 44(8): 818-825. doi: 10.3969/j.issn.1003-501X.2017.08.009

Citation:

Zengqiang Ma, Zibin Song, Yongsheng Wang. A method for detecting the wheel rail attack angle based on laser line detection[J]. Opto-Electronic Engineering, 2017, 44(8): 818-825. doi: 10.3969/j.issn.1003-501X.2017.08.009

A method for detecting the wheel rail attack angle based on laser line detection

College of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China

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Corresponding author: Zengqiang Ma, E-mail: mzqlunwen@126.com

Received Date 04 March 2017

Revised Date 28 June 2017

Published Date 15 August 2017

Abstract

Abstract

Attack angle is a key index to evaluate the stability of snakelike motion in the train. Due to the complexity of the running train and the small angle of attack, it is difficult to measure the angle between the wheel and rail. This paper presents a method for attack angle detection based on the laser line and the direction of motion as collinear wheel on the rail surface. The laser line and orbital edge line were obtained by image correction, Meanshift smoothing, and Radon line detection, and then the angle was calculated in the image. Comparison between simulation data and test results shows that the method can realize the detection of attack angle and it is simple and feasible at the same time. Finally, the correction method of the detection error is given, which increases the stability of the detection method. This method may lay the foundation for the evaluation of stability and safety of train operation.
- attack angle /
- snake motion /
- image detection /
- laser line

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References

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Overview

Overview

In the locomotive operation, because of the hunting motion caused by pure conical tread, a lateral force and complex creep force between the wheel and rail will emerge and result in attack angle between the wheel and rail when crossing a curve line. Although the attack angle is microscopic, it affects the wheel/rail contact loss and vehicle safety seriously and attack angle is a key index to evaluate the stability of snakelike motion in the train. Monitoring and analyzing the wheel/rail contact condition and the change of attack angle in the locomotive operation play a significant role in the stability and safety of vehicle operation. Due to the complexity of the running train and the small angle of attack, it is difficult to measure the angle between the wheel and rail. A method for combining the on-board camera with the laser line is presented to complete the image acquisition and detect the attack angle based on the laser line and the direction of motion as collinear wheel on the rail surface. The laser line and orbital edge line are obtained by some algorithms such as image pre-processing algorithm, image correction, Meanshift smoothing, and Radon line detection. The angle between the laser line and orbital edge in the image can be got through a series of image processing algorithms, which can reflect the attack angle in the running of locomotive. Radon detection algorithm is used to detect the relative position between laser line and rail edge line, and different conditions of undershooting changes are compared by simulation. The comparison between simulation data and experimental data shows that the method can simply realize the detection of attack angle and it is feasible enough. The results of detection illustrate that the change of attack angle is between 0.355 and -0.72 degrees, and the maximum error is 0.091 degrees. Finally, the correction method of the detection error and measurement accuracy analysis is given, which increases the stability of the detection method and demonstrates that it can meet the demand in engineering applications. It costs about 500 ms when the system completes primary detection of attack angle, which indicates that the detection speed is fast enough, and it can meet the detection requirements in engineering applications. However, some factors such as illumination and external vibration still need to be further studied to emphasize the robustness of the system. This method lays a foundation for further monitoring the condition of train operation and improving the safety mechanism of train.