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How Cognite Data Fusion accelerated condition monitoring for Wintershall Dea

Improved gas turbine monitoring unlocked hundreds of thousands of dollars in savings.

Wintershall Dea used Cognite Data Fusion (CDF) to combine contextualized data with domain expertise and develop machine learning models to monitor the installed gas turbine at the Mittelplate oil field, detecting faulty shutdowns and alerting maintenance engineers to avoid a potential breakdown during restart.

The solution saves Wintershall Dea an estimated $865,000 per incident.



In Short

Cognite and Wintershall Dea used Cognite Data Fusion, Asset Data Insight, and Model Hosting to operationalize machine learning models at scale, helping maintenance experts monitor the condition of the gas turbine.

Dollars saved
$865,000 saved per incident


The installed gas turbine generates electricity to power Wintershall Dea’s installation at the Mittelplate oil field off the coast of Germany. However, the maintenance engineers and electricians responsible for monitoring the turbine still rely on fixed thresholds for alarms and warnings. They have long lacked an easy way to receive early warnings about failures, which would enable the engineers to perform troubleshooting on the turbine and avoid breakdowns.


Cognite worked with Wintershall Dea and the turbine service company to liberate sensor data and events such work orders and collect all the information as a contextualized set in Cognite Data Fusion (CDF). The data is made easily accessible to users in Asset Data Insight, Cognite’s flagship application for smart maintenance.

Cognite and Wintershall Dea’s data scientists then used the contextualized data to build and operationalize machine learning models using CDF’s model hosting service. The outputs from the model are visualized in a Power BI dashboard. The solution provides electricians and maintenance engineers notifications and intuitive dashboards with data that describes the health of the shutdown, which help them better understand the condition of the turbine. From the respective landing pages, the dashboards let users explore sensor trends and warnings for each shutdown, early warnings of deviations from normal shutdowns, and analyses of historical shutdowns.



Wintershall Dea’s rotating equipment maintenance experts estimate that the dashboards will help the company save $865,000 per incident by automatically detecting unhealthy shutdowns, thereby eliminating unplanned downtime and repair costs. With greater access to data and insights, the maintenance and service engineers can move toward a condition-based maintenance strategy, thereby making Wintershall Dea’s operations safer and more reliable.

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