Turbine Blade Inspection AI

Add a real-time inspection layer to every blade with vision AI for turbine blade inspection. Built for the operations where one hairline crack at a blade root, coating patch spalled off a hot-section airfoil, or season of unchecked leading edge erosion can mean an engine pulled off-wing early, a turbine held offline through a forced outage, or a failure no one saw building. Whether you're inspecting jet engine blades at an overhaul shop, industrial gas turbine blades and vanes at a service center, or wind turbine blades in the factory and in the field, Roboflow extends your inspection coverage to every blade, with records that stand up in FAA Part 145 and EASA audits.

Gas Turbine and Engine Blade Defects:

• Catch hairline cracks and fatigue indications at blade roots, edges, and shrouds on every blade that crosses the bench
• Detect coating loss, spallation, burns, and hot corrosion on thermal barrier coatings across the airfoil
• Flag nicks, dents, FOD damage, and tip rub, graded consistently against your engine manual criteria

Wind Turbine Blades, from Factory to Field:

• Catch laminate wrinkles, voids, and dry fiber during blade manufacturing, before the blade ships
• Detect leading edge erosion, gel coat cracks, and lightning damage from drone and rope-access imagery across the fleet
• Track damage progression across inspection campaigns, so repair decisions ride on measured growth instead of memory

Overhaul Shops, Records, and Fleet Integration:

• Inspect at receiving, strip, and final assembly with consistent grading from the first blade of the shift to the last
• Keep a per-serial record of every blade with imagery and severity, ready for FAA Part 145, EASA, and customer audits
• Feed results into MRO software, CMMS, and fleet systems like IBM Maximo and SAP through REST and direct database writes, with repair-versus-scrap dispositions documented on every call

Bring intelligence to every blade today. Stop blade defects from becoming unscheduled removals, forced outages, or failures.

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More About Turbine Blade Inspection

What is turbine blade inspection with Vision AI?

Turbine blade inspection with vision AI uses computer vision models to detect and grade blade defects from imagery: cracks, coating loss, erosion, FOD damage, and corrosion on jet engine and industrial gas turbine blades, and laminate defects, leading edge erosion, and lightning damage on wind turbine blades. Models trained on your actual blade imagery, from overhaul bench photos to borescope stills to drone captures, apply your acceptance criteria consistently on every blade, with per-serial records that support FAA Part 145, EASA, and AS9110 documentation requirements.

Can Vision AI catch hairline cracks and early coating loss on turbine blades?

Hairline cracks at a blade root and the first patch of coating loss on a hot-section airfoil are small, low-contrast defects on reflective, complex geometry, and they are exactly what determines whether a blade flies another cycle or comes off the shelf. Deep-learning models trained on your blade imagery learn the visual signatures of early cracking, spallation, and erosion across coatings, alloys, and lighting conditions, grade severity consistently between inspectors and shifts, and attach imagery to every call so an inspector reviews the finding, not a description of it.

Does turbine blade inspection support FAA Part 145 and EASA requirements?

Yes. Roboflow models can run as documented inspection aids inside your FAA Part 145 and EASA Part 145 repair station processes, AS9100 and AS9110 quality systems, and IEC 61400-based wind programs, with per-blade records, imagery, model version history, and training data lineage for audits. Roboflow is the inspection engine; your certified inspectors own findings and dispositions, and the system keeps every call reviewable.

Can it integrate with our MRO software, drone platforms, and CMMS?

Yes. Roboflow Inference exposes a standard API, so blade inspection results flow into your existing systems: MRO and fleet software, CMMS and asset platforms like IBM Maximo and SAP, drone data platforms for wind fleet campaigns, and shop record systems, through REST, MQTT, and direct database writes. Inspection events carry blade serial, position, severity, and imagery, so repair routing, engineering review, and fleet trending all work from the same record, with a full audit trail behind every disposition.

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