A major oil and gas platform in Turkmenistan operated three turbine generators. Under normal conditions, two units run in load-sharing mode while the third remains on standby. Although a single generator could technically power the entire platform, the criticality of gas supply meant operators preferred to run at lower loads for additional safety margins.
However, one of the three generators became unavailable, leaving only two operational units. This forced the team to run a single generator at full load—an unacceptable risk. If that generator encountered any issue, the entire plant faced the possibility of a full power loss, process upsets, and expensive downtime.
The operations and engineering teams needed 100% reliability and uptime, with real-time visibility into emerging equipment problems and enough lead time to take action before failures occurred.
The customer deployed VROC AI predictive models to continuously monitor the online turbine generator and detect any early signs of degradation.
The AI was trained on historical turbine data, including five previous failure events, enabling it to learn the complex patterns leading up to generator trips and component failures. The model provided:
* Time-to-failure predictions
* Early anomaly detection
* Probable root causes
* Actionable maintenance insights
Early Warning: Air Intake Filter Blockage
Shortly after deployment, the model predicted a time to failure of 5.6 days for the active generator. Engineers investigated the AI-flagged root cause: increasing differential pressure across the air intake filter, indicating clogging that restricted airflow into the combustion system.
To avoid a trip, the team switched to the backup generator (#1) and shut down Generator 3. After three days they attempted to bring Generator 3 back online—but the AI once again predicted failure, this time in 3.7 days, confirming the underlying issue had not been fully addressed.
Corrective Action
The team executed a planned switchover between the generators and this time thoroughly cleaned the air intake filter. Once the generator returned online, the AI immediately reflected the improved conditions and predicted healthy, stable performance.
Since then, the AI has also identified other emerging issues, such as early warnings related to lube oil conditions, again supported by root-cause explanations and lead-time predictions.
* Zero unplanned trips since VROC AI was deployed
* No power interruptions or plant shutdowns due to generator failure
* Early detection of multiple issues including air intake filter blockage and lube oil degradation
* Operators gained days of advance warning, enabling controlled generator changeovers and planned maintenance
* Root-cause insights supported faster, more confident decision-making
* Continuous monitoring now protects not just the turbine generators but additional plant equipment, allowing teams to intervene earlier and avoid costly failures
The platform successfully met its mission: 100% uptime, no generator trips, and uninterrupted power supply for critical operations.
VROC AI continues to monitor performance in real time, ensuring ongoing reliability across the facility.
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The efficient deployment, continuous retraining of models with live data and monitoring of model accuracy falls under the categorisation called MLOps. As businesses have hundreds and even.
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