A Preliminary Study of Measuring Technique for Monitoring Wear Progress of Railway Wheel

Kuzuta, Masahito

doi:10.1299/jsmetld.2018.27.3201

Cited by 1 publication

(3 citation statements)

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“…However, measuring small defects, such as those with diameters of <1 mm, using laser sensors is challenging, and false positives may occur when dust or rust on wheel surfaces is detected by machine vision. Kuzuta (2018) and Umehara and Miyahara (2017) measured the wheel tread dimension using laser measurement; however, their methods are different from our proposal. Kuzuta's method proposed the installation of instruments on a railcar.…”

Section: Increased Noise and Vibration During Runningmentioning

confidence: 90%

“…Thus, a combined approach using a laser sensor and machine vision is necessary. Previous laser measurement methods (Kuzuta, 2018;Umehara and Miyahara, 2017) alone are insufficient, and warrant integration with machine vision. This study shows that noncontact measurements, such as laser measurement and machine vision, are promising for defect detection.…”

Section: Discussionmentioning

confidence: 99%

“…elongation of wear or fracture. Recording the measurement results enables to understand the elongation of wear or fracture (Kuzuta, 2018), and these results can be used for predicting the progress of wheel tread surface defects.…”

Section: Wheel Tread Dimension Measurement Under Simulated Conditionsmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Increased Noise and Vibration During Runningmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%