Compressor health and reliability
Challenge
- The compressor system is a core element of the process flow, and maintaining uptime is critical.
- High frequency of failures due to complex assembly and moving parts.
- Facility sensors provide isolated data rather than system-level insights.
Solution
This advanced machine learning solution monitors and assesses compressor systems using statistical methods and multiple specialized models for anomaly detection.
Reliable: Improves operational safety and reliability by providing a comprehensive system overview featuring key time-series metrics such as pressure and temperature.
Efficient: Increases the time between failures by analyzing risk indices and predicting potential issues before they occur.
Time-efficient: Prevents undesired events and improves system uptime, reducing production losses through early alarms and proactive maintenance.
Compressor health is critical in oil and gas operations, where downtime or failure can lead to significant production losses, safety risks, and costly repairs. Advanced monitoring solutions leveraging ML enable proactive maintenance, ensuring optimal performance and extending the lifespan of these vital assets. (NOC).
Results
Alerts issued up to five hours before targeted failures. Downtime prevention and improved planning to avoid production disruption.
Compatible solution with different compression systems for scalable deployment.
