&lt;b&gt;Contactless Measuring Method of Overhead Contact Line Positions by Stereo Image Measurement and Laser Distance Measurement&lt;/b&gt;

Nezu, Kazuyoshi; Matsumura, Itaru; Aboshi, Mitsuo; Niwakawa, Makoto; Kawabata, Takuro; Tabayashi, Seiji

doi:10.2219/rtriqr.56.181

Cited by 11 publications

(8 citation statements)

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“…In addition, some solutions combined the image processing technology and the MLS technology to build up OCS inspection systems. For instance, Meidensha Corporation [33,34] combined laser scanners and line scan cameras to detect the position of contact wires. MERMEC Company [35,36] developed an inspection system that is made up of lasers and cameras to monitor the overhead wires and pick up defects.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning-Based Method for Overhead Contact System Component Recognition Using Mobile 2D LiDAR

Chen

Lin

et al. 2020

Sensors

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The overhead contact system (OCS) is a critical railway infrastructure for train power supply. Periodic inspections, aiming at acquiring the operational condition of the OCS and detecting problems, are necessary to guarantee the safety of railway operations. One of the OCS inspection means is to analyze data of point clouds collected by mobile 2D LiDAR. Recognizing OCS components from the collected point clouds is a critical task of the data analysis. However, the complex composition of OCS makes the task difficult. To solve the problem of recognizing multiple OCS components, we propose a new deep learning-based method to conduct semantic segmentation on the point cloud collected by mobile 2D LiDAR. Both online data processing and batch data processing are supported because our method is designed to classify points into meaningful categories of objects scan line by scan line. Local features are important for the success of point cloud semantic segmentation. Thus, we design an iterative point partitioning algorithm and a module named as Spatial Fusion Network, which are two critical components of our method for multi-scale local feature extraction. We evaluate our method on point clouds where sixteen categories of common OCS components have been manually labeled. Experimental results show that our method is effective in multiple object recognition since mean Intersection-over-Unions (mIoUs) of online data processing and batch data processing are, respectively, 96.12% and 97.17%.well record geometric details of surrounding objects [5]. Therefore, it is appropriate to apply MLS systems to OCS inspections. The operational condition of OCS can be acquired by analyzing the MLS point cloud instead of manual measurement. Recognizing the point cloud of OCS as a critical task of the data analysis has been studied in previous studies (e.g., [6][7][8]). Mobile 2D LiDAR is a special kind of MLS system applied to railway inspections. Figure 1 shows an instance of the mobile 2D LiDAR used to scan the OCS infrastructure. As for this kind of MLS system, a point cloud is built up by integrating points at each 2D scan line. Fully understanding the point cloud is significant for automatic inspections and intelligent diagnoses. Thus, this study focuses on multiple OCS component recognition with mobile 2D LiDAR. However, it is a difficult task because the similarities and the various associations among OCS components make scenes complex [8]. In this case, model-driven and data-driven methods become incompetent because rules and features for recognizing specific objects are difficult to be designed by human beings. Fortunately, the success of deep learning-based image recognition (e.g., [9][10][11]) has promoted the fast development of deep learning-based point cloud segmentation, which provides novel means to understand point cloud with semantics.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning-Based Method for Overhead Contact System Component Recognition Using Mobile 2D LiDAR

Chen

Lin

et al. 2020

Sensors

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“…The Measurement can be achieved in a static way (OCL is in its static state without pantograph interaction), or dynamically (pantograph-catenary interaction) with low contact force and train velocity. Nevertheless, some advanced solutions enable dynamic measurement with commercial speeds using a train mounted measurement system (Nezu et al, 2015). The sag value is checked regarding to a threshold value defined by standards that allow the trains to reach the desired speed.…”

Section: Geometry Datamentioning

confidence: 99%

Prognostics and Health Management for an Overhead Contact Line System - A Review

Brahimi

Medjaher

Mohammed

et al. 2020

IJPHM

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The railway industry in European countries is standing a significant competition from other modes of transportation, particularly in the field of freight transport. In this competitive context, railway stakeholders need to modernize their products and develop innovative solutions to manage their asset and reduce operational expenditures. As a result, activities such as condition-based and predictive maintenance became a major concern. Under those circumstances, there is a pressing need to implement prognostics and health management (PHM) solutions such as remote monitoring, fault diagnostics techniques, and prognostics technologies. Many studies in the PHM area for railway applications are focused on infrastructure systems such as railway track or turnouts. However, one of the key systems to ensure an efficient operability of the infrastructure is the overhead contact line (OCL). A defect or a failure of an OCL component may cause considerable delays, lead to important financial losses, or affect passengers safety. In addition maintaining this kind of geographically distributed systemsis costly and difficult to forecast. This article reviews the state of practice and the state of the art of PHM for overhead contact line system. Key sensors, monitoring parameters, state detection algorithms, diagnostics approaches and prognostics models are reviewed. Also, research challenges and technical needs are highlighted

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“…As shown in Fig. 3, the contactless measuring device for overhead contact lines [1,2] is installed on the roof of a vehicle to measure the static three-dimensional geometry of the overhead contact lines. Laser scanners and line cameras are used for sensing.…”

Section: Advanced Equipment Condition Monitoring Technologymentioning

confidence: 99%

<b>Recent Research and Development Activities in Maintenance Technologies for Electric Railway Power Supply Systems</b>

Ikeda¹

2020

QR of RTRI

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In order to ensure reliability and stability of the electric railway systems, adequate maintenance operation for the electric power supply system is an essential matter. On the other hands, its maintenance operation involves high cost, because this system has a huge spatial scale. Furthermore, working population in Japan has been decreasing today, resulting in high demands on improving efficiency of the maintenance operation. RTRI has therefore been tackling the issue of improving in quality, efficiency and cost of the maintenance operation for the electric power supply system. This paper describes recent RTRI's research and development activities on the maintenance technologies for the power supply system in order to overcome these issues.

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<b>Contactless Measuring Method of Overhead Contact Line Positions by Stereo Image Measurement and Laser Distance Measurement</b>

Cited by 11 publications

References 0 publications

A Deep Learning-Based Method for Overhead Contact System Component Recognition Using Mobile 2D LiDAR

A Deep Learning-Based Method for Overhead Contact System Component Recognition Using Mobile 2D LiDAR

Prognostics and Health Management for an Overhead Contact Line System - A Review

<b>Recent Research and Development Activities in Maintenance Technologies for Electric Railway Power Supply Systems</b>

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