Defect Detection of Pantograph Slide Based on Deep Learning and Image Processing Technology

Wei, Xiukun; Jiang, Siyang; Li, Yan; Li, Chenliang; Jia, Limin; Li, Yongguang

doi:10.1109/tits.2019.2900385

Cited by 87 publications

(39 citation statements)

References 14 publications

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“…Some criteria for evaluating the surface condition of the pantograph were discussed in this unit. If we express the first wear depth in the collector strips of the pantograph as Ad, then the maximum wear depth can be expressed as in Equation 1 [4].…”

Section: B Labelling Imagesmentioning

confidence: 99%

“…where, A(x) represents the amount of wear at position x. L is the length of the collector strip. The second criterion is surface roughness, denoted by R. This can be calculated by Equation 2 [4].…”

Section: B Labelling Imagesmentioning

confidence: 99%

“…From the pixels obtained by image processing methods, the thickness of the collector strips can be found. If the maximum thickness of the strip is considered to be 40 mm, then the thickness can be calculated from the image with Equation 3 [4].…”

Section: B Labelling Imagesmentioning

confidence: 99%

“…The power required for the operation of electric trains, whose usage prevalence has increased gradually in recent years and is expected to continue increasing, is obtained from pantograph-catenary systems. Pantographs, one of the basic components of the pantograph-catenary system, provides electrical power to the locomotive by contacting the catenary while the train is running [4]. Pantographs draw electrical power from the catenary using plates called current collector strips, which are made of wear-resistant materials such as carbon or graphite [5].…”

Section: Introductionmentioning

confidence: 99%

“…They used 257 pantograph images and divided them into four classes. Wei et al [4] divided the failures in the pantograph collector strips into four classes and detected them with a deep learning network. They also used image processing techniques.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Based Method for Detecting of Wear on the Current Collector Strips’ Surfaces of the Pantograph in Railways

Karaduman

Akın

2020

IEEE Access

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In pantographs, current collector strips transmit the electrical energy they receive from the catenary to the locomotive and provide the necessary power for the locomotive's movement. In order for the current collector strips to transmit electricity to the locomotive in a healthy way, their surface must be smooth. Wear on the surface of the current collector strips reduces conductivity and can create arcs, endangering the health and safety of the pantograph and catenary system. In this paper, a Convolutional Neural Network (CNN) architecture is developed to detect wear on the current collector strips. Images obtained from pantographs used on railways were created with a clean and improved data set using Hough Transform and Power Law Transform. The created dataset contains 909 pantograph images. This dataset was trained and tested with both the developed CNN architecture and classic deep learning architectures (ResNet50, VGG16). The experimental results show that the developed CNN architecture's training results and test results are more successful than classical architectures.

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Section: B Labelling Imagesmentioning

confidence: 99%

Section: B Labelling Imagesmentioning

confidence: 99%

Section: B Labelling Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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