Pantograph Arc Detection of Urban Rail Based on Photoelectric Conversion Mechanism

Yu, Xiaoying; Su, Hongsheng

doi:10.1109/access.2020.2966493

Cited by 12 publications

(7 citation statements)

References 15 publications

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“…The non-contact detect is realized by observing and analyzing the image of the pantograph and catenary through the image sensor on the roof of the train, which can give a more intuitive analysis of the interaction of the pantograph and catenary, but it requires a higher reliability of the algorithm. The main application is based on non-visible light for arc detection [10][11][12], and research using visible light data mostly focuses on the acquisition of online video sequences, combined with manual observation or offline processing. The online feature localization method under the vehicle platform is affected by image complexity, which leads to key problems given the robustness required by the online operation of PCS contact analysis, and has attracted research attention.…”

Section: Introductionmentioning

confidence: 99%

Online Intelligent Perception of Pantograph and Catenary System Status Based on Parameter Adaptation

2021

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Online autonomous perception of pantograph catenary system status is of great significance for railway autonomous operation and maintenance (RIOM). Image sensors combined with an image processing algorithm can realize the automatic acquisition of the pantograph catenary condition; however, it is difficult to meet the demand of long-term stable condition acquisition, which restricts the implementation of online contact state feedback and the realization of railway automation. This paper proposes an online intelligent perception of the pantograph and catenary system (PCS) status based on parameter adaptation to realize fast and stable state analysis when the train is in long-term operation outdoors. First, according to the feature of the contact point, we used histogram of gradient (HoG) features and one-dimensional signal combined with a KCF tracker as the baseline method. Then, a result discriminator located by L1 and hash similarity constraints was used to construct a closed-loop parameter adaptive localization framework, which retrieves and updates parameters when tracking failure occurs. After that, a pruned RefineDet method was used to detect pantograph horns and sparks, which, together with the contact points localization method, ensure the long-term stability of feature localization in PCS images. Then, based on the stereo cameras model, the three-dimensional trajectory of the whole pantograph body can be reconstructed by the image features, and we obtained pantograph catenary contact parameters including the pantograph slide posture, contact line offset, arc detection, separation detection, etc. Our method has been tested on more than 16,000 collected image pairs and the results show that the proposed method has a better positioning effect than the state-of-art method, and realizes the online acquisition of pantograph catenary contact state, representing a significant contribution to RIOM.

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Section: Introductionmentioning

confidence: 99%

Online Intelligent Perception of Pantograph and Catenary System Status Based on Parameter Adaptation

2021

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“…Arcing problems also occur on wind farms [33] or in nuclear power systems [34]. In terms of powering moving objects, railway traction [35][36][37][38][39], automotive [40,41], ship [42], and aircraft [43,44] power systems also suffer from this problem. Railway traction is especially interesting in this regard, because arcs also occur during a normal operation in the pantograph-catenary contact area, what is important from a power quality point of view [37].…”

Section: Introductionmentioning

confidence: 99%

“…Electric arcs generate noises at very different frequencies, which can be detected by various sensors [45]. If the place of the arc presence is known, magnetic sensors [23], radio antennas [24,25], IR, visual light, or UV [36,44,46,47] sensors can be used; otherwise, the current and voltage in the protected system can be measured. Then, the measured data need to be processed.…”

Section: Introductionmentioning

confidence: 99%

A Novel Algorithm for Fast DC Electric Arc Detection

Dołęgowski

Szmajda

2021

Energies

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Electric arcing is a common problem in DC power systems. To overcome this problem, the electric arc detection algorithm has been developed as a faster alternative to existing algorithms. The following issues are addressed in this paper: The calculation of the proposed algorithm of incremental decomposition of the signal over time; the computational complexity of Fast Fourier Transform (FFT) and the incremental decomposition; the test bench used to measure electric arcs at given parameters; the analysis of measurements using FFT; and the analysis of measurements using incremental decomposition. The parameters are the DC voltage, electric load, and width of the gap between electrodes. The results showed that the proposed algorithm allows for a faster calculation—about seven times faster than FFT—and cheaper implementation in electric arc detection devices than FFT.

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“…In their study, they performed an arc detection after determining the head area of the pantograph. Yu et al [8] proposed a method based on the photoelectric transformation mechanism for arc detection in pantographs. In addition, Qu et al [9] proposed a method based on the Adadelta deep neural network and used a genetic optimization method to predict the state of pantograph and catenary.…”

Section: Introductionmentioning

confidence: 99%

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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Pantograph Arc Detection of Urban Rail Based on Photoelectric Conversion Mechanism

Cited by 12 publications

References 15 publications

Online Intelligent Perception of Pantograph and Catenary System Status Based on Parameter Adaptation

Online Intelligent Perception of Pantograph and Catenary System Status Based on Parameter Adaptation

A Novel Algorithm for Fast DC Electric Arc Detection

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

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