Improve OEE Through Predictive Maintenance

By Tommy Mitchell, Principal & Global Practice Leader, Process Industries, Kalypso: A Rockwell Automation Business

Facing the double imperatives of maintaining operational efficiency and ensuring safety in harsh offshore environments, a company in the energy industry recognized the need to depart from traditional maintenance schedules.

The company was looking to optimize equipment uptime by reducing unnecessary downtime, enhance operational safety and environmental compliance, and lower overall maintenance costs by eliminating unneeded checks.

To address these needs, Rockwell Automation and Kalypso: A Rockwell Automation Business,  implemented a predictive maintenance solution adapted specifically for a critical semi-submersible drilling rig.

Predictive Maintenance Solution

The project commenced with a thorough analysis and a pilot program on a semi-submersible rig designed for year-round drilling operations.

The implementation involved several advanced technological interventions:

1. Health Index Development. A dynamic health index was created to continuously evaluate the condition of the drilling equipment, providing a quantifiable measure of operational integrity.
2. Anomaly Detection. Advanced statistical models were used to identify deviations from normal operation patterns, facilitating early detection of potential issues.
3. Real-Time Monitoring and Decision Support. By integrating edge computing, the system provided real-time data processing directly on the rig, minimizing latency and enhancing the responsiveness of the maintenance team.
4. Intuitive Dashboards. These dashboards displayed critical health metrics and alerts, providing the rig operators with immediate and actionable insights.

Future Impact and Long-term Strategy

The predictive system detects potential issues early and triggers timely alerts to the maintenance teams to address problems before they impact operations. This proactive approach maximized rig availability and productivity.

Encouraged by the pilot's success, the company plans to scale the predictive maintenance model across its offshore fleet to standardize efficiency and safety practices, focusing on:

Scalability: Applying the model to additional rigs and platforms.

Adaptive Enhancements: Continuously refining the system through machine learning and feedback loops to adapt to new challenges and data insights.

This collaboration is an example of combining emerging industrial analytics capabilities with established technologies to drive operational equipment excellence for capital-intensive industries.

Our work made it possible to transition from reactive to predictive maintenance. Real-time monitoring and edge computing allow the energy firm to now optimize performance across critical offshore assets.

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