Refinery – Predictive Analytics for Rotating Equipment
A North American refinery relied heavily on critical rotating equipment—compressors, pumps, turbines, and fans—to maintain safe and continuous operations. Unexpected failures were increasing due to aging assets, inconsistent monitoring practices, and data scattered across historian systems, vibration platforms, CMMS records, and OEM reports. These failures created costly downtime, elevated safety risks, and rising maintenance expenses. Leadership needed a unified, predictive approach to identify equipment issues earlier and reduce unplanned outages.
We implemented the predictive analytics platform which significantly improved reliability, safety, and maintenance efficiency across rotating equipment:
20% reduction in rotating equipment failures across targeted units
Earlier detection of vibration deviations and thermal abnormalities
Reduced unplanned downtime, increasing production stability
Lower maintenance spends through targeted interventions instead of blanket PMs
Improved safety margin, especially for high-energy equipment
THE SITUATION: Rotating equipment in refining environments generates high-frequency operational and vibration data, but insights were trapped across isolated OT and IT systems. Analysts relied on manual reviews to interpret vibration signatures and process deviations, resulting in delayed detection of early warning signals. Differences in equipment types, operating conditions, and reporting practices led to inconsistent interpretation of asset health. The refinery lacked a centralized view of risk, making proactive maintenance nearly impossible.
THE PROBLEM: The refinery struggled with data fragmentation, inconsistent monitoring, and limited predictive capability, including:
Siloed OT/IT data (historian, vibration systems, CMMS, inspections)
Limited early warning for failure precursors
Manual monitoring by vibration analysts and reliability engineers
Inconsistent interpretation of equipment health signals
No unified view of risk across rotating assets
Inability to correlate conditions across process, equipment, and environmental factors
The Frame Solution
Deployed an advanced predictive analytics solution that unified equipment health data, introduced machine learning–based prediction models, and enabled more proactive and consistent reliability practices across the refinery.
Unified sensor data, historian signals, vibration analytics, and CMMS history into a Manufacturing Data Architecture (MDA)
Built ML-based failure prediction models using vibration signatures, temperature, pressure, motor load, and process variables
Developed anomaly detection for early-warning alerts based on equipment-specific behavior
Integrated findings into reliability dashboards with risk scores and recommended actions
Connected predictive insights to maintenance workflows, generating prioritized work orders
