How Cognite provided fast, accurate condition monitoring for Aker BP

Cognite Data Fusion was set up to interoperate with the Siemens IMS and dashboards.

The holy grail of predictive maintenance is a real prospect thanks to real-time condition monitoring, as used by Aker BP's onshore team.

In short

Siemens used DataOps provided by Cognite, including Cognite Data Fusion (CDF), to enable real-time condition monitoring for Aker BP's Ivar Aasen onshore team.

How Cognite provided fast, accurate condition monitoring for Aker BP-01
1,300 hours
Time saved by Siemens due to smooth integration directly with Cognite Data Fusion.
How Cognite provided fast, accurate condition monitoring for Aker BP-02
400,000+ documents collected, read and contextualized
How Cognite provided fast, accurate condition monitoring for Aker BP-03
20 billion data points available via one secure entry point


Aker BP granted Siemens access to all Ivar Aasen field data. Siemens pulled process data through their control system and most of the third-party equipment data bypassing the control system and going straight to the IMS. However, there remained several other systems, usually inaccessible to Siemens, that held potentially valuable data, such as work orders, work permits, document systems, HMS information, etc. Combining these sources would vastly enrich their solution.

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Siemens turned to Cognite, whose cloud-native industrial data platform was already in operation across Aker BP’s assets on the NCS. Cognite Data Fusion offered the capability to collect, clean, and contextualize more various kinds of data automatically and without space limitations. With Aker BP’s authorization, Siemens redirected their system to read the company’s data from the CDF, including data types that had previously been outside of Siemens’s scope. For Ivar Aasen, this meant 150,000 time series, 20 billion data points, 400,000+ documents made available through a single, secure point of entry.

Siemens estimates that attempts to run point-to-point integrations with all the individual source systems and ingest their data would have taken about 1,500 hours. The process of integrating with the CDF alone--pulling in all the same data--took only 200 hours. And a lot of that time was actually spent looking at the data and deciding how best to visualize it.

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Artificial intelligence, machine learning, and tested algorithms are what make the Siemens IMS visualizations intuitive and effective. With rapid access to live and historical data, regardless of original source or format, via the CDF’s single point of entry, Siemens was ready to present their powerful processing, analysis, and display capabilities to Aker BP.

Armed with the power of prediction, they can also:

  • Boost safety by limiting the number of human personnel on the platform
  • Order fewer replacement parts and reduce the “just in case” stock
  • Deploy the right crew and tools when maintenance needs do arise
  • Bring down the cost of the consequences of equipment failure (i.e., unplanned downtime or production slowdown)

Especially in offshore environments, condition-based maintenance (and eventually, predictive maintenance) has the potential to revolutionize business models and reduce bottom lines.

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