Rolls-Royce uses AI turbine blade inspection and engine health monitoring to prevent 400 maintenance events a year

Overview

Rolls-Royce uses Microsoft Cloud for Manufacturing, Azure Databricks, and generative AI across engine design, turbine production, and engine health monitoring. Its 'Signature Analyzer' AI/machine learning system inspects turbine blade cooling holes (previously about 2 million inspected manually per month), using machine vibration analysis and generative AI to optimize defect detection, boosting machine utilization 30% and accelerating fault resolution from days to near real time. AI-powered engine health monitoring tracks more than 10,000 engine parameters and detects and prevents around 400 unplanned maintenance events a year, saving millions in repair costs.

The challenge

Rolls-Royce wanted to improve productivity and reduce costs across its design, build, and operate functions. Manual inspection of turbine blade cooling holes was incredibly time-consuming and often created a production bottleneck.

The solution

Rolls-Royce uses Microsoft Cloud for Manufacturing and technology stacks such as Microsoft Azure Databricks, Unity Catalog, and high-powered GPUs to accelerate engine design. Its "Signature Analyzer" system uses machine vibration analysis and generative AI to optimize turbine blade defect detection, and AI-powered engine health monitoring (via the Engine Vibration Health Monitoring Unit) tracks more than 10,000 engine parameters for predictive maintenance.

Reported business value

This has boosted machine utilization by 30%, significantly minimized erroneous scrap from the manual inspection process, and accelerated fault resolution from days to near real time. Across all fleets, Rolls-Royce detects and prevents around 400 unplanned maintenance events a year, saving millions in repair costs and minimizing disruptions for customers.