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Data-driven asset lifetime extension

Kapernikov helped Elia to obtain an accurate overview of the condition of its network equipment and to anticipate potential risks of failure. We applied several database analysis techniques to support the company with its asset management strategy.

Challenge

Belgian electricity grid operator Elia has set ambitious goals in its Asset Management Excellence program. The company wanted to optimize the asset maintenance process, reduce OPEX, CAPEX and maintenance workload. At the same time, Elia wanted to guarantee the safety and the quality of the grid. However, the data about the company’s assets was scattered across various systems and needed to be retrieved and consolidated. To have an accurate view on all its assets and to be able to make data-driven decisions, Elia turned to Kapernikov for help.

Role

Kapernikov provided Elia with a complete overview of the condition of its vast network of assets. First, we organized asset data according to technology, age, function on the grid, failure history, and executed maintenance. To allow the data to be automatically updated, we created a data lake, a type of data repository that offers flexible data processing.

After collecting, cleaning and linking all the data, we developed data visualizations in order to have a good understanding of the asset network and its failures. We used data mining techniques to enrich the forecasts for each piece of equipment, according to its lifetime condition, installation, maintenance, etc. This way, we were able to attribute a health index to each asset. This health index reflects the condition of this asset and is an important criterion for conditional maintenance.

Results

Elia now has better insight into the expected workload for assets that need to be maintained or replaced. This has allowed the company to evolve from time-based to conditional maintenance of its assets. Thanks to this higher efficiency in asset management, Elia will be able to extend the lifetime of its power transformers by 20% and save several millions of euros in the coming years.

This data-driven analysis has supported Elia in its strategic decisions and allowed the company to better manage and understand the risks of equipment failure. Possible incidents can now be anticipated and avoided. It also allows Elia to plan and optimize the financial and human capital that is needed for asset maintenance and replacement.

* The data used for this showcase dashboard is no real Elia data.

Applied solutions

Used technologies

R
Microsoft Power BI

D3
MS SQL server