Leading Fertilizer Company Avoids 460 Hours of Annual Downtime with AI Predictive Maintenance

The world's largest single-site urea exporter, producing large volumes of urea and ammonia annually, took a predominantly reactive approach to maintaining an aging fleet of compressors, turbines, and other critical assets. Frequent unplanned downtime forced costly emergency repairs, and reliability and performance losses ran 60% higher than operational targets.

Over several months, BakerHughesC3.ai (BHC3) configured BHC3 Reliability to enable predictive monitoring for 27 production assets across 4 plants, integrating over 5 years of historical data and live sensor data from the Baker Hughes Cordant Platform — extensive historical records and substantial volumes of incremental records from 2,400 sensors daily. The team trained 233 ML models for anomaly detection across 4 key asset types.

The solution delivered a 1.8% increase in asset uptime and an average predictive lead time of 62 days for predictable events, allowing maintenance crews to plan instead of react. The system avoided 460 hours of downtime per annum. The company is planning to scale from 27 to an additional 92 assets.

• A 62-day predictive lead time for equipment failures transforms maintenance from reactive emergency response to planned, cost-effective intervention. • Integrating extensive historical records with 2,400 live sensors enables the training of 233 models that cover the full range of critical asset failure modes. • Starting with compressors and turbines as the highest-impact asset types and planning to scale to 92 more assets provides a proven expansion blueprint for large process manufacturers.