Multi-Scale Hierarchical Conditional Random Field for Railway Electrification Scene Classification Using Mobile Laser Scanning Data

With the recent rapid development of high-speed railway in many countries, precise inspection for railway electrification systems has become more significant to ensure safe railway operation. However, this time-consuming manual inspection is not satisfactory for the high-demanding inspection task, thus a safe, fast and automatic inspection method is required. With LiDAR (Light Detection and Ranging) data becoming more available, the accurate railway electrification scene understanding using LiDAR data becomes feasible towards automatic 3D precise inspection. This thesis presents a supervised learning method to classify railway electrification objects from Mobile Laser Scanning (MLS) data. First, a multi-range Conditional Random Field (CRF), which characterizes not only labeling homogeneity at a short range, but also the layout compatibility between different objects at a middle range in the probabilistic graphical model is implemented and tested. Then, this multi-range CRF model will be extended and improved into a hierarchical CRF model to consider multi-scale layout compatibility at full range. The proposed method is evaluated on a dataset collected in Korea with complex railway electrification systems environment. The experiment shows the effectiveness of proposed model

Prognostics and health management for an overhead contact line system - A review

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 systems is 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

Taguchi based Design of Sequential Convolution Neural Network for Classification of Defective Fasteners

Fasteners play a critical role in securing various parts of machinery. Deformations such as dents, cracks, and scratches on the surface of fasteners are caused by material properties and incorrect handling of equipment during production processes. As a result, quality control is required to ensure safe and reliable operations. The existing defect inspection method relies on manual examination, which consumes a significant amount of time, money, and other resources; also, accuracy cannot be guaranteed due to human error. Automatic defect detection systems have proven impactful over the manual inspection technique for defect analysis. However, computational techniques such as convolutional neural networks (CNN) and deep learning-based approaches are evolutionary methods. By carefully selecting the design parameter values, the full potential of CNN can be realised. Using Taguchi-based design of experiments and analysis, an attempt has been made to develop a robust automatic system in this study. The dataset used to train the system has been created manually for M14 size nuts having two labeled classes: Defective and Non-defective. There are a total of 264 images in the dataset. The proposed sequential CNN comes up with a 96.3% validation accuracy, 0.277 validation loss at 0.001 learning rate.Comment: 13 pages, 6 figure

Power Quality in Electrified Transportation Systems

"Power Quality in Electrified Transportation Systems" has covered interesting horizontal topics over diversified transportation technologies, ranging from railways to electric vehicles and ships. Although the attention is chiefly focused on typical railway issues such as harmonics, resonances and reactive power flow compensation, the integration of electric vehicles plays a significant role. The book is completed by some additional significant contributions, focusing on the interpretation of Power Quality phenomena propagation in railways using the fundamentals of electromagnetic theory and on electric ships in the light of the latest standardization efforts

Multimodal deep learning for point cloud panoptic segmentation of railway environments

The demand for transportation asset digitalisation has significantly increased over the years. For this purpose, mobile mapping systems (MMSs) are among the most popular technologies that allow capturing high precision three-dimensional point clouds of the infrastructure. In this paper, a multimodal deep learning methodology is presented for panoptic segmentation of the railway infrastructure. The methodology takes advantage of image rasterisation of the point clouds to perform a rough segmentation and discard more than 80% of points that are not relevant to the infrastructure. With this approach, the computational requirements for processing the remaining point cloud are highly reduced, allowing the process of dense point clouds in short periods of time. A 90 km-long railway scenario was used for training and testing. The proposed methodology is two times faster than the current state-of-the-art for the same point cloud density, and pole-like object segmentation metrics are improved.Fundación BBVAAgencia Estatal de Investigación | Ref. PID2019-108816RB-I00Ministerio de Universidades | Ref. FPU20/01024Universidade de Vigo/CISU

Metrology Infrastructure for Energy and Power Quality in DC Railway Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Railway operations in icing conditions: a review of issues and mitigation methods

This article focuses on studying the current literature about railway operations in icing conditions, identifying icing effects on railway infrastructure, rolling stock, and operations, and summarizing the existing solutions for addressing these issues. Even though various studies have been conducted in the past on the impact of winter, climate change, and low temperatures on railway operations, not much work has been done on optimizing railway operations under icing conditions. This study demonstrates that further research is needed to better understand ice accretion and its effects on different parts of railways. It appears that railway infrastructure faces serious problems during icing conditions, and additional research in this field is required to precisely identify the problems and suggest solutions. Therefore, it is important to enhance the knowledge in this area and suitable optimal and cost-effective ice mitigation methods to minimize icing effects on railway operations and safety

Continuous gauging of electric traction contact wire

Includes bibliographical references.In this system the electric traction overhead contact wire wear is continuously measured. A GaAs LED and lens combination produces a collimated beam of light along the pantograph pan. The beam, through which the contact wire slides, is monitored by a vertical array of phototransistors spaced at equal intervals. The number of phototransistors in shadow at any instant is proportional to the contact wire thickness. A fibre-optic data link conveys the encoded information from the pan at high tension to the locomotive at ground potential

Estimating Workforce Development Needs for High-Speed Rail in California, Research Report 11-16

This study provides an assessment of the job creation and attendant education and training needs associated with the creation of the California High-Speed Rail (CHSR) network, scheduled to begin construction in September 2012. Given the high profile of national and state commitment to the project, a comprehensive analysis that discusses the education, training, and related needs created during the build out of the CHSR network is necessary. This needs assessment is achieved by means of: 1) analyzing current high-speed rail specific challenges pertaining to 220mph trains; 2) using a more accurate and robust “bottom-up” approach to estimate the labor, education, skills, and knowledge needed to complete the CHSR network; and 3) assessing the current capacity of railroad-specific training and education in the state of California and the nation. Through these analyses, the study identifies the magnitude and attributes of the workforce development needs and challenges that lie ahead for California. The results of this research offer new insight into the training and education levels likely to be needed for the emergent high-speed rail workforce, including which types of workers and professionals are needed over the life of the project (by project phase), and their anticipated educational level. Results indicates that although the education attained by the design engineers of the system signifies the most advanced levels of education in the workforce, this group is comparatively small over the life of the project. Secondly, this report identifies vast training needs for the construction workforce and higher education needs for a managerial construction workforce. Finally, the report identifies an extremely limited existing capacity for training and educating the high-speed rail workforce in both California and in the U.S. generally

Structural analysis of railways bolster-beam under commercial operation conditions: Over-traction and over-braking

The conditions for the operation of railway systems are closely related to the increase of the commercial demand; as a consequence, the performance of the structural elements of railways changes. The present paper focuses on a study of the structural behaviour of bolster-beams under commercial operation conditions of railway systems, specifically in the dynamic conditions generated in events of over-traction and over-braking on the vehicle running. The proposed work is constructed based on the following phases: (i) analysis of the kinematics of the vehicle; (ii) development of numerical models, a model based on the multibody theory, and a Finite Elements model; (iii) development of experimental field tests; and (iv) development of simulations for a detailed analysis of the structural behaviour for a study of the strain distribution in the main bolster-beam. This study is applied to a particular case of a railway system that provides commercial service to passengers