Condition monitoring of wooden railway sleepers

Yella, Siril; Dougherty, Mark; Gupta, N. K.

doi:10.1016/j.trc.2008.06.002

Cited by 51 publications

(33 citation statements)

References 13 publications

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“…31 But the fault inspection system proposed in this paper is completely automatic, and has been installed in many railway stations in China, such as Wuhan, Guilin, Chengdu, and Beijing. Although our system shows satisfactory performances, it may still be improved by using various sensor equipment and multidimension information fusion techniques.…”

Section: Resultsmentioning

confidence: 99%

Automatic online vision-based inspection system of coupler yoke for freight trains

Zheng

Wei

2016

J. Electron. Imaging

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, "Automatic online vision-based inspection system of coupler yoke for freight trains," J. Electron. Imaging 25(6), 061602 (2016), doi: 10.1117/1.JEI.25.6.061602. Abstract. Fault inspection plays an important role in ensuring the safe operation of freight trains. With the development of computer vision technology, the vision-based fault inspection has become one of the principal means of fault inspection. A coupler yoke is an important component of the train's connection system, and faults in this system would cause the separation of the train, leading to a serious accident. We propose an automatic image inspection system to inspect for faults in coupler yokes during the running of a freight train. The inspection process is divided into two parts: the localization part and the recognition part. In the localization part, we propose multiple dimension features, design a fast algorithm to compute multiresolution image features, and use a linear support vector machine classifier to locate the position of the coupler yoke. In the recognition part, we propose a fast decision tree training method by prepruning noneffective features, and use Adaboost decision trees as the final fault classifier. Experimental results show that this proposed method can achieve a fault inspection rate of 98.6% while the average processing time of an image is about 98 ms, which shows our system has a high inspection accuracy and a good real-time performance.

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Section: Resultsmentioning

confidence: 99%

Automatic online vision-based inspection system of coupler yoke for freight trains

Zheng

Wei

2016

J. Electron. Imaging

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“…In Turkey, railway sleeper visual inspections are currently performed manually by human operators, i.e. a railway worker walks along the track and visually examines each sleeper, [17,23]. Two most frequent defects of concrete sleepers are cracks and surface defects (TCDD 2009).…”

Section: Sleepersmentioning

confidence: 99%

Optimisation of railway track maintenance and renewal works by genetic algorithms

Lakušić¹

2017

JCE

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This paper describes a new approach to optimisation of railway track maintenance and renewal works via genetic algorithms. Decision support systems and genetic algorithms are used in this study to optimise railway track maintenance and renewal activities. The results obtained in the scope of this study show that planned maintenance and renewal management systems can be developed and successfully used instead of resorting to corrective maintenance activities. Optimizacija održavanja i remonta željezničkih kolosijeka primjenom genetičkih algoritama Rad opisuje novi pristup optimizaciji radova na održavanju i remontu željezničkih kolosijeka pomoću genetičkih algoritama. U istraživanju su primijenjeni sustavi za potporu u odlučivanju i genetički algoritmi radi optimizacije radova na održavanju i remontu željezničkih kolosijeka. Rezultati istraživanja su pokazali da je moguće razviti sustav za upravljanje radovima planskog održavanja i remonta te ga uspješno primijeniti umjesto korektivnih radova održavanja.

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“…France, the United States, Germany, Japan and other countries have long been committed to the use of computer graphics technology to achieve visual inspection of track structure components [3] . Among them, the Italy researchers Mazzeo and his partner have designed a fastener system nut missing detection device, which has achieved some results by now [4][5][6] . However, this research uses the neural network to classify the feature, it is easy to fall into the local optimum, besides, it needs a lot of training sample [2] .…”

Section: Introductionmentioning

confidence: 99%

Detection of Rail Fastener Based on Wavelet Decomposition and PCA

Xiao¹,

Min²,

Ma³

2017

dtcse

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Abstract.A new method based on wavelet decomposition and principal component analysis (PCA) is proposed to solve the problem that the traditional method cannot effectively and quickly detect the rail fastening nut. Through the wavelet decomposition of the rail fastener image, the high frequency component of the image can be removed, and the noise can be reduced and the running time of the algorithm can be reduced. The principal component analysis method is used to reduce the dimension of the image, and the minimum distance classifier is used to detect the rail fastener. The experimental results show that the proposed algorithm can effectively detect the missing state of rail fastener, and the algorithm is robust to the occlusion of the noise image and rail fastener image.

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Condition monitoring of wooden railway sleepers

Cited by 51 publications

References 13 publications

Automatic online vision-based inspection system of coupler yoke for freight trains

Automatic online vision-based inspection system of coupler yoke for freight trains

Optimisation of railway track maintenance and renewal works by genetic algorithms

Detection of Rail Fastener Based on Wavelet Decomposition and PCA

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