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USE CASE
1月 2019
USE CASE

How Siemens and Cognite partnered to provide fast, accurate condition monitoring

The interoperation of Cognite Data Fusion with the Siemens IMS and dashboards enables real-time Condition Monitoring for Aker BP's Ivar Aasen onshore team and pushes the industry toward the lucrative possibility of Predictive Maintenance.
How Siemens and Cognite partnered to provide fast, accurate condition monitoring
The interoperation of Cognite Data Fusion with the Siemens IMS and dashboards enables real-time Condition Monitoring for Aker BP's Ivar Aasen onshore team and pushes the industry toward the lucrative possibility of Predictive Maintenance.

In short

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.

The interoperation of Cognite Data Fusion with the Siemens IMS and dashboards enables real-time Condition Monitoring for Aker BP's Ivar Aasen onshore team and pushes the industry toward the lucrative possibility of Predictive Maintenance.

  • 1,300 hours

    Time saved by Siemens due to smooth integration directly with the Cognite Data Platform.

Challenge

Challenge

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.

Solution

Siemens turned to Cognite, whose cloud-native industrial data platform was already in operation across Aker BP’s assets on the NCS. The Cognite Data Platform 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 CDP, 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 CDP 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.  

Solution
Impact

Impact

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 CDP’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.