Abstract
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.
Highlights
Over recent years, electrified railways have developed from traditional industrial upgrades to those encompassing improvements to the system’s speed, automation, safety, and comfort
Shen et al [19] proposed an online contact point trajectory tracking method based on the 3D trajectory to analyze the pantograph and catenary system (PCS) contact state, achieving real-time high-precision measurement and analysis, which effectively solves the current problem in the fault detection of PCS
We enable the detection of pantograph horns, sparks, and other features through the pruned RefineDet method [20], reconstructing the three-dimensional trajectory of the pantograph slide to acquire the pantograph catenary contact parameters, which include pantograph pose, center line offset, PCS detachment, and arcing detection
Summary
Over recent years, electrified railways have developed from traditional industrial upgrades to those encompassing improvements to the system’s speed, automation, safety, and comfort. Shen et al [19] proposed an online contact point trajectory tracking method based on the 3D trajectory to analyze the PCS contact state, achieving real-time high-precision measurement and analysis, which effectively solves the current problem in the fault detection of PCS. We focus on the difficulties of online pantograph catenary image feature localization and propose an online intelligent perception of the PCS state based on parameter adaption. We propose the addition of a result discriminator to the closed-loop tracking method so that the corresponding parameter adjustment can be carried out independently through positioning result feedback This ensures that tracking and positioning can be achieved under a variety of light conditions under long-term train operation. The method we proposed can effectively obtain the PCS contact parameters and contact state analysis during the long-term outdoor operation of trains, supporting the safe and stable operation of railways.
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