Impact

Shift from reactive fixes to predictive reliability for 160+ turbines with AI-powered anomaly and failure detection.

Background

A global energy technology provider needed to move from reactive maintenance to a data-driven, predictive approach for 160+ industrial turbines. We delivered an AI-powered prototype that detects anomalies, explains sensor correlations, and predicts failures in advance—even with limited historical data.

Solution Highlights

• Anomaly Detection Engine: Uses unsupervised learning to detect and flag unusual patterns in continuous sensor data streams.
• Correlation Analysis Module: Quantifies relationships among sensor readings to highlight contributing factors to system behavior.
• Predictive Failure Modeling: Prototypes an AI model predicting turbine failures hours in advance, even with limited labeled data.
• Data Augmentation for Sparse Logs: Fills gaps in historical SCADA and event logs to enable model training despite incomplete records.
• Explainable AI Outputs: Provides clear insights on which sensor patterns and factors most influence critical output parameters.
• Prototype Accuracy with Limited Data: Achieves up to 89% recall for trip prediction 2 hours ahead and explains critical output with <5% error rates.
• Scalable Design for Future Enhancements: Built to evolve with more data over time, improving accuracy and supporting longer lead-time predictions.

Key Benefits

• Early Failure Alerts: Predict turbine failures up to 2 hours in advance for reduced downtime.
• Better Maintenance Planning: Improved planning for maintenance crews and parts.
• Increased Reliability: Higher equipment reliability and reduced unplanned outages.
• Sensor-Level Insights: Greater visibility into sensor relationships and failure drivers.
• Scalable Foundation: Foundation for scaling to full predictive maintenance solutions.