On-Machine Measurement of Wheel Tread Profile With the 1-D Laser Sensor

Liu, Fei; Li, Lin; Chao-zhen, Hou; Xu, Guizhi; Liú, Dan; Zhang, Binbin; Wang, Lixin; Chen, Xiaohang; Du, Hongfeng

doi:10.1109/tim.2021.3122186

Cited by 13 publications

(6 citation statements)

References 17 publications

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“…The second view ( Figure 8 b) shows the completely random line patterns generated by the different filters used in the manual scanning. This effect of curvature on the measurement quality of sensors similar to those studied has been reported by other authors [ 38 , 39 ] in the measurement of surfaces with a large variation in curvature, such as gear profiles.…”

Section: Experimentationsupporting

confidence: 84%

Laser Triangulation Sensors Performance in Scanning Different Materials and Finishes

Meana,

Zapico,

Cuesta

et al. 2024

Sensors

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The variety of equipment implementing laser triangulation technology for 3D scanning makes it difficult to analyse their performance, comparability, and traceability. In this study, three laser triangulation sensors arranged in different configurations are analysed using high precision spheres made of different materials and surface finishes. Three types of reference parameters were used: diameter, form error, and standard deviation of the point cloud. The experimentation was based on studying the quality of the point clouds generated by the three sensors, which enabled us to find and quantify an edge effect in the horizon of the scanned surface. A procedure to reach the optimal filtering conditions was proposed, and a chart of recommended usage of each sphere (material and finish) was created for the different types of sensors. This filter enables removal of both spurious points and those few points that spoil the form error, greatly improving the quality of the measurement.

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Section: Experimentationsupporting

confidence: 84%

Laser Triangulation Sensors Performance in Scanning Different Materials and Finishes

Meana,

Zapico,

Cuesta

et al. 2024

Sensors

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“…Laser sensors are used to compare the original CAD data and measured data because they are less affected by environmental light fields than CCD cameras. Liu et al (2021) and Li et al (2017) measured wheel tread dimensions using laser sensors. However, they only measured profile moments, which are only sufficient for measuring wheel diameters.…”

Section: Increased Noise and Vibration During Runningmentioning

confidence: 99%

“…Researchers have used different types of sensors for automatic inspection (Emoto et al, 2021;Jinlong et al, 2016;Liu et al, 2021;Li et al, 2017). Each approach has its own advantages and disadvantages, and a concrete method has not been established.…”

Section: Increased Noise and Vibration During Runningmentioning

confidence: 99%

A method for detecting wear and damage on railcar wheel tread surface using a combination of laser measurement and machine vision

EMOTO,

EMARU,

RAVANKAR

et al. 2024

Mechanical Engineering Journal

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Skilled technicians primarily use manual tools to maintain the geometry and surface conditions of railcar wheels. To ensure efficient wheel maintenance, the development of an automated inspection system is required. Herein, an inspection method for railcar wheels is proposed based on the assessment of wheel tread dimensions and surface conditions and investigation results of the method are presented. The parameters of wheel tread dimensions and surface conditions are considered because they are crucial for railway operation safety and manually inspecting them requires a large workforce. To improve the efficiency of the system, a combined inspection method through laser measurement and machine vision is used, both the techniques compensate for each other's shortcomings. The experiments are conducted using laser sensors and machine vision techniques, including defect detection using AI model based on YOLOv5, as well as image data captured via a digital video camera to obtain high-accuracy automatic measurements and determine the feasibility of such measurements. Additionally, the measurement accuracy of the proposed method is verified based on experiments on sample wheel having wheel tread surface defects. In this paper, sample wheel including various wheel tread surface defects is used.

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“…For an accurate knowledge of the geometry profile feature of the tread wheel, a mechanism based on on-machine measurement by utilizing a 1D laser sensor is investigated by Liu et al [42]. With the combination of specific tread profile morphology and laser sensor measurement model, the correspondent on-machine measurement model of the wheel tread profile was developed.…”

Section: Laser and Cameramentioning

confidence: 99%

State-of-the-Art Wayside Condition Monitoring Systems for Railway Wheels: A Comprehensive Review

et al. 2023

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In recent decades, there has been a constant demand for faster, longer, and safer railway networks. This also brings challenges for condition monitoring systems in modern railway vehicles. More specifically, critical parts of railway vehicles like wheels degrade over time due to various operational and environmental reasons. Different dynamic effects such as skidding/sliding over the track and the presence of contamination between wheel-rail cause various wheel defects. Faulty wheels ultimately lead to the derailment of railway vehicles. To avoid worst situations like railway derailments, various research has been conducted for developing efficient condition monitoring systems for railway wheels. In addition, there have been some commercial condition monitoring products that can be deployed with railway vehicles. These systems incorporate various sensors such as strain gauges and vision sensors to collect data for diagnosis and prognosis. Various methods have been explored but yet there is a broad research gap in terms of developing advanced onboard condition monitoring systems. With the progress in technology, advanced systems with Machine Learning/Deep Learning methods can provide more efficient and robust condition monitoring of dynamic railway systems. Considering the need for advancement in condition monitoring systems for railway vehicles, a comprehensive review of existing condition monitoring systems for railway wheels is conducted in this paper. The review is aimed at understanding the feasibility and potential of new methods for modern railways. This paper provides a detailed overview of studies on the existing wayside systems and reports their advantages and disadvantages concerning its recently emerging counterpart on-board monitoring systems. Data acquisition systems and analysis methods are critically reviewed which could assist in developing more efficient and reliable condition monitoring systems for railway wheels. This article also reviews the current progress of wayside systems and their limitations. The article is targeted at the researchers and engineers working in this domain, who can pave the way for developing advanced and cost-effective condition monitoring systems for railway wheels using modern technologies.

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On-Machine Measurement of Wheel Tread Profile With the 1-D Laser Sensor

Cited by 13 publications

References 17 publications

Laser Triangulation Sensors Performance in Scanning Different Materials and Finishes

Laser Triangulation Sensors Performance in Scanning Different Materials and Finishes

A method for detecting wear and damage on railcar wheel tread surface using a combination of laser measurement and machine vision

State-of-the-Art Wayside Condition Monitoring Systems for Railway Wheels: A Comprehensive Review

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