A modelling approach for railway overhead line equipment asset management

The Overhead Line Equipment (OLE) is a critical sub-system of the 25kV AC overhead railway electrification system. If OLE asset management strategies can be evaluated using a whole lifecycle cost analysis that considers degradation processes and maintenance activities of the OLE components, the investment required to deliver the level of performance desired by railway customers and regulators can be based on evidence from the analysis results. A High Level Petri Net (HLPN) model, proposed in this paper, is used to simulate the degradation, failure, inspection and maintenance of the main OLE components and to calculate various statistics, associated with the cost and reliability of the system over its lifecycle. The HLPN considers all the main OLE components in a single model and it can simulate fixed frequency inspections and condition-based maintenance regimes. In order to allow the relevant processes to be modelled accurately and efficiently, the HLPN features are used, such as specific data about individual components is taken account of in the general model. The HLPN, developed using international standards, is described in detail and a framework of its analysis for reliability and lifecycle cost evaluation is proposed. In this novel whole system model different OLE component types and their instances on a line are modelled simultaneously, and the dependencies are considered in terms of opportunistic inspection and maintenance. An example HLPN for the catenary wire is used to illustrate the model, and an application of the methodology for whole lifecycle cost evaluation of a two-mile OLE line is presented

Apprenticeship Standard: High Speed Rail and Infrastructure (HSRI) Advanced Technician

Dissertação de Mestrado em Gestão da Formação e Administração Educacional apresentada à Faculdade de Psicologia e Ciências da Educação da Universidade de Coimbr

Development of a prognostics and health management system for the railway infrastructure – Review and methodology

The Prognostics and Health Management (PHM) can be considered as a key process to deploy a predictive maintenance program. Since its inception as an engineering discipline, a lot of diagnostics and prognostics algorithms were developed and furthermore methodologies for health management and PHM development established. These solutions were applied in a lot of industrial cases aiming a maintenance transformation. In the Aerospace and Military systems, for example, the PHM has been applied more than 20 years with systems and components applications. During this last decade, the railway industry focused on maintenance issues and expressed a special interest on the PHM systems. The maintenance of the railway infrastructure requires considerable resources and an important budget. Many of the developed algorithms and methodologies can be imported to the Rail Transport systems. However, a methodology to develop a PHM system for a railway infrastructure must be established. This paper provides an overview on the key steps to design a PHM system regarding to the specific characteristics of the railway infrastructure. In addition, tools and procedures for each level of the PHM process are reviewed, as well as a summary of the existing monitoring, health assessment and decision solutions for the railway infrastructure

Digital twinning of railway overhead line equipment from airborne lidar data

The automated generation of geometry-only digital twins of Overhead Line Equipment (OLE) system in existing railways from point clouds is an unsolved problem. Currently, this process is highly reliant upon manual inputs, needing 10 times more labour hours than scanning the physical asset. The resulting modelling cost counteracts the expected benefits of the digital twin. We tackle this challenge using a novel model-driven method that exploits the highly regulated and standardised nature of railways. It starts by restricting the search for OLE elements relative to point clusters of the railway masts. The resulting point clusters of the OLE elements are then converged with various parametric models of different catenary configurations to verify the presence of OLE elements and to find the best possible fit. The method outputs a geometry-only digital twin of the OLE system in Industry Foundation Classes (IFC) format. The method was tested on an 18 km railway point cloud and achieves overall detection rates of 93.2% F1 score for OLE cables and 98.1% F1 score for other OLE elements. The accuracy of the generated model is evaluated using distance-based metrics between the ground truth model and the automated model. The average modelling distance is 3.82 cm Root Mean Square Error (RMSE) for all 18 km dataCambridge Commonwealth, European & International Trust Bentley Systems UK Plc

Smart maintenance and inspection of linear assets: An Industry 4.0 approach

Linear assets have linear properties, for instance, similar underlying geometry and characteristics, over a distance. They show specific patterns of continuous inherent deteriorations and failures. Therefore, remedial inspection and maintenance actions will be similar along the length of a linear asset, but because as the asset is distributed over a large area, the execution costs are greater. Autonomous robots, for instance, unmanned aerial vehicles, pipe inspection gauges, and remotely operated vehicles, are used in different industrial settings in an ad-hoc manner for inspection and maintenance. Autonomous robots can be programmed for repetitive and specific tasks; this is useful for the inspection and maintenance of linear assets. This paper reviews the challenges of maintaining the linear assets, focusing on inspections. It also provides a conceptual framework for the use of autonomous inspection and maintenance practices for linear assets to reduce maintenance costs, human involvement, etc., whilst improving the availability of linear assets by effective use of autonomous robots and data from different sources