Imagine sending a doctor to check every single blade on a wind farm — except the doctor has to dangle from ropes hundreds of feet up, spending five hours per turbine. That's how blade inspection still works today. This project built a smart drone that flies itself around all three blades in one go, snaps detailed pictures, spots cracks and damage automatically using machine learning, and sends you a report you can actually act on. It's like replacing a dangerous, slow stethoscope with an instant full-body MRI scan — from the air.
Wind turbine blade inspection is dangerous, slow, and expensive. Operators currently pay around €1500 per turbine for inspectors who rope down each blade manually — a process that takes 5 hours and keeps the turbine offline. With O&M costs already eating 25% of total energy costs per kWh, the industry desperately needs faster, cheaper, and safer inspection methods.
The project built a commercial prototype of an autonomous drone payload capable of inspecting all 3 wind turbine blades in a single flight, plus a first industrial version ready for pilot trials. The system includes autonomous flight with collision avoidance, machine learning-based automatic fault detection, and the BladeInsight cloud reporting platform for actionable customer reports.
If you are a wind farm operator spending €1500 and 5 hours per turbine on manual rope-access inspections — this project developed an autonomous drone system that inspects all 3 blades in a single flight, delivering a 50% direct cost saving and 6X reduction in inspection downtime. The machine learning software automatically detects faults and generates actionable reports through a cloud platform.
If you are managing a portfolio of wind assets and struggling with inspection backlogs and unpredictable maintenance costs — this system provides consistent, automated fault detection across your fleet. The BladeInsight cloud platform delivers standardized reporting, helping you move from reactive to predictive maintenance and protect your 25% O&M cost share of total energy cost.
If you are an inspection services company looking to scale your wind energy offering without hiring more rope-access specialists — this turnkey drone payload with autonomous flight, collision avoidance, and automated fault detection lets you inspect more turbines with fewer experts. The system was built to commercial prototype stage with an industrial version ready for pilot trials.
The project data states a 50% direct cost saving compared to existing methods. Traditional rope-access inspection costs approximately €1500 per turbine and takes 5 hours. The autonomous drone system also decreases turbine inspection downtime by 6X.
The system was designed for scalability — it inspects all 3 blades in a single autonomous flight, removing the need for dedicated flight control experts. The machine learning algorithms continuously improve fault detection from a growing image database, making it more accurate at scale. The BladeInsight cloud platform provides centralized reporting across your fleet.
The project was funded under the EIC SME Instrument Phase 2, with BLADEINSIGHT SA (Portugal) as the sole partner and IP owner. Based on available project data, the company also prepared an Investor Relations Kit, suggesting they are open to commercial partnerships or investment. Contact through SciTransfer for licensing or partnership discussions.
The project produced a commercial prototype of the drone payload and built a first industrial version ready for pilot trial demonstrations. This puts the technology at a pilot-ready stage. The company website (bladeinsight.com) indicates continued commercial activity beyond the project period.
No. A key feature is fully autonomous flight with collision avoidance — the system does not require dedicated experts for flight control. Fault detection and report generation are also automated through software and machine learning, removing the need for image processing specialists.
Based on available project data, the project does not detail specific regulatory compliance. However, commercial drone operations in Europe fall under EASA regulations. The autonomous flight capability and collision avoidance features suggest the system was designed with operational safety requirements in mind.
The system uses optical sensors and machine learning algorithms trained on a growing database of blade images to automatically detect flaws and defects. It generates actionable reports through the BladeInsight cloud platform, replacing both manual rope inspection and inferior ground-camera methods that miss faults due to poor image quality.
This is a single-company project by BLADEINSIGHT SA, a Portuguese SME that received EIC SME Instrument Phase 2 funding. The 100% industry consortium with no university or research partners signals a commercially driven venture rather than an academic exercise. The solo structure means all IP sits with one company, simplifying any licensing or partnership discussions. The investor relations kit deliverable confirms the company is actively seeking commercial growth beyond EU funding.
BLADEINSIGHT SA is a Portuguese SME. Contact SciTransfer for a warm introduction to the team.
Want to test this drone inspection system on your wind farm or explore a partnership? SciTransfer can arrange a direct introduction to the BLADEINSIGHT team and help structure the engagement.
