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SAMBA - Smarter Assets Management with Big Data

SAMBA - Smarter Assets Management with Big Data

Published 21 March 2017

The project has a large potential for improving the transmission system's asset management by optimizing decisions on maintenance and reinvestments using developments in information and communication technology (ICT) and existing business specific knowledge.

Optimizing asset management decisions implies more predictive and risk based maintenance and reinvestments with potential for large socio-economic benefits. Today, condition and remaining lifetime data is not readily available for analysis and decision-making at Statnett. There is a large potential for added value by activating more online, automatically collected condition and system operation data which combined with maintenance data collected on site will be crucial for estimating asset's condition, probability of failure and remaining lifetime.

Large-scale exploration of data will demand new ICT-solutions. Therefore, important objectives and the main research challenges are to develop a system architecture, data models and analysis methods for big data driven asset management.

The restructuring of the asset management system and introduction of a new data handling structure for a TSO is a large and costly task. The SAMBA project facilitates knowledge sharing between Statnett, SINTEF ER and some of the main international companies specialized on asset management systems. This pre-competitive dialog will reduce risk exposure when building and/or procuring such systems.

This project is important for Statnett in a period of unpreceded development caused both by aging assets increasing the need for reinvestments and by many new capacity increasing projects.

Smarter Asset Management using Big dAta (SAMBA) is a 3-year industry innovation project headed by Statnett, partially founded by the Norwegian Research Council and involving several partners: SINTEF Energy ResearchGE Grid Solutions, ABB and IBM.

Research Scientist

Project duration

2016 - 2018