Predictive Maintenance for Turbine Engines
Unscheduled maintenance was costing millions in penalties and flight delays. Existing sensors detected faults too late to prevent grounding.
A top-tier engine manufacturer needed to reduce the cost of power-by-the-hour contracts by preventing unplanned engine removals.
Digital Twin Engine
Physics-based models calibrated with live sensor data for each serial number.
Anomaly Detection
Unsupervised learning catching vibration patterns invisible to rule-based logic.
Maintenance Prescriber
Recommending specific part replacements to ground crews.
False Positives
Alerting too often would destroy trust; models had to exceed 95% precision.
Data Volume
Processing 2TB of flight data per day required edge-to-cloud pipelines.
We built a hybrid AI-physics digital twin for each engine. It analyzes vibration, heat, and pressure in real-time to forecast component life, alerting crews to repairs weeks before failure.
