Industry specific software package

Tender description

Approximately 30 % of Network Rail infrastructure has 25 kV overhead line equipment installed to provide power to electric traction vehicles. This infrastructure is monitored and maintained using methods including visual inspections, manual measurements and train borne monitoring. There is an opportunity to monitor the infrastructure on a more frequent basis using technology fitted to in-service vehicles. Network Rail would like to transition from a ‘find and fix’ approach to one of ‘predict and prevent’ by developing an improved condition monitoring system that can identify contact forces, geometric defects and other failure modes of the OLE. The purpose of this RFI is to engage with the supply base and request information relating to the OLE dynamic pattern recognition software/tools available in the market. This RFI aims to inform Network Rail of the current market capability and any future development work which may be required. Network Rail expects that the software tool will perform analysis using on-board video camera data. The scope of this RFI does not cover the installation of the camera equipment itself, however provisions to gain access to the video should be included. The software tool shall demonstrate capabilities in analysing the on-board camera data in the following areas: 1) Measuring the dynamic heights and stagger of the contact wire as the pantograph passes. This can be compared with static position and over time an engineering signature can be developed for the system to assess actual performance against design. This intelligence can be used to identify locations with a high risk of dewirement and improve reliability and availability of the system. 2) Identifying locations where the contact wire exceeds design heights and stagger. This will identify areas with excessive negative staggers and areas where the pantograph is operating at its limits of OLE interaction; edges of the horns, maximum operable height etc. which are areas with greater risk. 3) Identifying crossovers and locating the contact wire interface with the pantograph at crossovers. There have been several high profile dewirements with modern open horn pantograph designs at locations where wires converge. By looking at the transfer of pantograph contact from one wire to another the safety and performance of the system could be improved. 4) Identifying the dynamic gauge of any pantograph and Defective Heel settings. This will highlight any areas with encroachment from fixed infrastructure into design tolerances and thereby enabling preventive maintenance to be carried out. 5) measuring the dynamic contact force on the pantograph. Which will supplement measurement by instrumentation but with higher velocity and volume allowing more correlation. Ultimately reliance on instrumentation may reduce as calibration of machine vision system improves. The data output shall be provided on either: (a) A dedicated remote platform provided by the supplier; and/or (b) In a format to be incorporated into Network Rail’s Intelligent Infrastructure platform. It is envisaged at role out that there will be a requirement for approximately 30 users of the technology requiring either software licenses or access to the remote platform. Should you wish to respond to this RFI please follow the below instructions: 1) Follow the link — https://networkrail.bravosolution.co.uk/web/login.html 2) From the welcome screen, access the ‘View Current Opportunities’ link and select ‘Pre-Market Engagement: OLE Dynamic Pattern Recognition System’; 3) Click on the attachment – ‘OLE Dynamic Pattern Recognition — RFI Submission Template’; 4 Once completed please send your submission document via email as attachment to briony.toogood@networkrail.co.uk with the email subject ‘OLE Dynamic Pattern Recognition System RFI’. The response deadline for this RFI is 17.00 on 20.12.2019. Further information on the requirement can be found within the BravoNR opportunity. Any questions or clarifications in respect of this RFI should be addressed to briony.toogood@networkrail.co.uk.