Wheel Tread Defects Inspection Based on SVM

Guo, Guangyu; Peng, Jianping; Yang, Kai; Xie, Liming; Song, Wenwen

doi:10.1109/fendt.2017.8584565

Cited by 8 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The stress-based method is the most commonly used wheel flat detection method as its advantages of low cost, convenient installation, and high accuracy [89]. However, the traditional wheel flat detection system based on the stress method can only detect the wheel-rail contact area, which will cause more than 70% of the wheel tread area to be undetectable [96]. In addition, the size of the flat cannot be directly reflected by the acquired waveform, thus it is necessary to build models to acquire the relationship between flat size and the waveform.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

Feng

et al. 2023

Sensors

View full text Add to dashboard Cite

Wheel flats are amongst the most common local surface defect in railway wheels, which can result in repetitive high wheel–rail contact forces and thus lead to rapid deterioration and possible failure of wheels and rails if not detected at an early stage. The timely and accurate detection of wheel flats is of great significance to ensure the safety of train operation and reduce maintenance costs. In recent years, with the increase of train speed and load capacity, wheel flat detection is facing greater challenges. This paper focuses on the review of wheel flat detection techniques and flat signal processing methods based on wayside deployment in recent years. Commonly used wheel flat detection methods, including sound-based methods, image-based methods, and stress-based methods are introduced and summarized. The advantages and disadvantages of these methods are discussed and concluded. In addition, the flat signal processing methods corresponding to different wheel flat detection techniques are also summarized and discussed. According to the review, we believe that the development direction of the wheel flat detection system is gradually moving towards device simplification, multi-sensor fusion, high algorithm accuracy, and operational intelligence. With continuous development of machine learning algorithms and constant perfection of railway databases, wheel flat detection based on machine learning algorithms will be the development trend in the future.

show abstract

Section: Discussionmentioning

confidence: 99%

“…In 2017, a system of collecting wheel tread defect images for online running trains was designed by Guangyu Guo et al [96]. 16 high speed CCD cameras were used in the system to acquire the images of the entire wheel tread.…”

Section: Image-based Methodsmentioning

confidence: 99%

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

Feng

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Each approach has its own advantages and disadvantages, and a concrete method has not been established. Using cameras and machine vision, Fang (2021) and Guo et al (2017) presented an autonomous examination method for railways. Machine vision is a valid method for measuring surface defects, and many researchers have studied wheel and rail surface defects using machine vision Pan et al, 2019;Zhang et al, 2010;Yang et al, 2014;.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…The train wheel tread defect [1] is a common irregular surface defect formed in the operation process of vehicles, which directly and seriously affects the running state and safety of the train. Therefore, the tread defect detection [2] is an urgent question to be solved.…”

Section: Introductionmentioning

confidence: 99%

A segmentation algorithm for train wheel tread defect area based on attention mechanism

Jiang

Yang²,

Qiu³

et al. 2022

Thirteenth International Conference on Information Optics and Photonics (CIOP 2022)

View full text Add to dashboard Cite

The defect of the train wheel tread is a threat to its safe driving, and the defect detection of the tread is an important work. The extraction of defect area is a crucial link. In this paper, we propose a segmentation algorithm of tread defect area based on attention mechanism, which realizes the more accurate segmentation of tread defect area.This algorithm uses U-net as the backbone network, firstly, introduces the Lovasz-Softmax loss, secondly, CBAM is introduced between the encoder and decoder. Get the attention feature map information in the channel and space dimensions, and then multiply the two feature map information with the original input feature map to make adaptive feature correction to obtain a more accurate feature map and improve the accuracy of the segmentation algorithm.Validated on the dataset of train wheel tread, and the experimental results show that the algorithm PA is 99.54% and mIoU is 98.27%, which improves by 0.83% and 0.73% compared with Unet algorithm, which verifies the effectiveness of the algorithm.

show abstract

Wheel Tread Defects Inspection Based on SVM

Cited by 8 publications

References 7 publications

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

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

A segmentation algorithm for train wheel tread defect area based on attention mechanism

Contact Info

Product

Resources

About