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Projects: Projects for Investigator
Reference Number NIA_NGGT0089
Title Asset Information Models (AIM) for Component/Pattern Recognition
Status Completed
Energy Categories Fossil Fuels: Oil Gas and Coal(Oil and Gas, Refining, transport and storage of oil and gas) 100%;
Research Types Applied Research and Development 100%
Science and Technology Fields ENVIRONMENTAL SCIENCES (Geography and Environmental Studies) 100%
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Project Contact
No email address given
National Grid Gas Transmission
Award Type Network Innovation Allowance
Funding Source Ofgem
Start Date 01 March 2016
End Date 01 March 2017
Duration 12 months
Total Grant Value £662,000
Industrial Sectors Technical Consultancy
Region London
Programme Network Innovation Allowance
 
Investigators Principal Investigator Project Contact , National Grid Gas Transmission (100.000%)
Web Site http://www.smarternetworks.org/project/NIA_NGGT0089
Objectives The generation of accurate operational records / documents that can capture and reference key operational data Geographically positioned assets and asset models Establishment, interrogation and reporting of asset / operational information i.e. data mining of the Asset Information Model Aid the identification, understanding and management of the asset health issues and the delivery of any associated projects Provide a consistent format and interface for the asset owner / operator Success from the trial would be a demonstration of saving through the building of a retrospective 3D model of an existing site using component recognition. Longer term, a key success criterion is to create a library of complex gas components which can be identified using enhanced shape recognition. This makes it possible to target a reduction in excess of 25% of the time taken to create a 3D model from a laser scan.
Abstract National Grid asset records traditionally exist in a 2D electronic or hard copy format, and are dependent upon continuous transfer of asset data to remain accurate, involving time spent both searching and updating the records. This is required to capture changes from activities such as maintenance, asset modification, and asset replacement, and is a manual process than can be inefficient as well as often inaccurate. The aim is to address these issues to reach a method that is accurate and efficient for updating records. The Project aims to establish a methodology for efficiently creating asset records in a 3D model format using a laser scan, and transferring the data from the project model to form an asset information model (AIM). This builds on the previous BIM (Building Information Modelling) project. Deliverables for the project include: Develop detailed workflows for the combined use of laser scanning techniques and relevant software Develop a National Grid specification for asset scanning and use of component/pattern recognition software to define deliverable/output requirements Develop the National grid base class component libraries that allow the component /pattern recognition software to function Review and compare other available component / pattern recognition software packages, to include future direction and alignment Develop the Asset Information Modelling coding and specification Develop Asset Information Models for two defined trial sites Investigate hybrid model approach i.e. convert assets with high value asset data and use raw scan images for non-converted items with low value asset data Visits to Dresden to discuss software development with Faro Produce fly through sequence for both trial sites of the completed model Update existing National Grid laser scanning specification and development with Lessons Learnt Bi-monthly engagement meetings/presentations with relevant stakeholders In addition, the knowledge transfer to all framework suppliers of how the efficiency is to be achieved; to be completed through a client specification and final recommendations report.Note : Project Documents may be available via the ENA Smarter Networks Portal using the Website link above
Publications (none)
Final Report (none)
Added to Database 10/07/18