Our client owns and operates an oil rig in the North Sea. When operating the rig it is important to ensure the equipment remains reliable and production is optimised, as any drops in production or failures in equipment have significant repercussions. These repercussions are further exacerbated by the remote operation as well as the recent COVID pandemic, which has seen a drop in oil price and a hightened risk around safety. Our clients reliability team have been using the VROC AI predictive maintenance platform for a number of months and have been able to catch critical deviations, giving them valuable time to intervene and prevent the failures from taking place.


VROC successfully detected and predicted a failure in our client's Generator. The VROC AI predictive maintenance models detected an abnormal bearing temperature increase, and predicted the likely hood of an event to occur at 100%.


Our client's reliability team tried restarting the generator, however this did not fix the issue and the VROC model continued to predict a failure. Unfortunately the generator tripped on high temperature lube oil, which was being caused by a blockage in the lube oil cooler. The cooler system did not have remote sensing, which meant it was not possible to model this particular component. An accurate bearing temperature model reacted to the cooler problem and indirectly predicted the issue, alerting the client team to the problem.

After fixing the cooler blockage, the system returned to normal operation and the VROC predictive maintenance model no longer showed a red warning.

Our client's team were alerted to the issue with the Generator before it happened. With proper remote sensing we would have been able to identify the root cause and save more than 2000 barrels of oil by preventing a shutdown.

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