AI Turbine Blade Inspection
The Problem
“Slow, inconsistent turbine blade defect detection”
Organizations face these key challenges:
1
Manual image/video review is slow and constrained by scarce expert inspectors, creating bottlenecks during outages
2
Inconsistent defect identification and sizing across technicians, sites, and OEMs leads to variable maintenance decisions and risk tolerance
3
Limited ability to trend defect growth across inspection cycles, causing either premature part replacement or late intervention and forced outages
Impact When Solved
50-80% reduction in inspection review and reporting time with standardized defect tagging10-30% reduction in forced outage risk through earlier identification of crack initiation, erosion hotspots, and coating loss4-12 hours shorter outages per event, translating to ~$0.2M-$1.5M avoided cost per inspection depending on unit size and power prices