HCL : DL based defect detection on wind turbine

HCL : DL based defect detection on wind turbine

Reducing the Levelized Cost of Energy (LCoE) remains the priority in the development of the wind energy sector. In the last decades, both the size and capacity of wind turbines have increased by virtue of technological developments in wind energy field. This situation resulted in an increasing focus on topics such as wind turbine fault detection. The Operation and Maintenance (O&M) costs account for 20 - 25% of the total LCoE. The aim of our technologies is to achieve more effective operation, inspection and maintenance of wind turbines with minimal human interference. This solution uses drone technology to capture images and DNN to find out defects present. The model is optimized using Intel® Distribution of OpenVINO™ and New Intel® Xeon® Scalable Processors to give faster inference.

*Please note that member solutions are often customizable to meet the needs of individual enterprise end users.



  • Optimized inference
    This solution utilizes the power of Intel® Distribution of OpenVINO™ toolkit to get highspeed inference.
  • Detection of small-scale defects
    The DNN model is capable of detecting small structural defects.



WorldwideBrazilChina (PRC)FranceGermanyIndiaJapanKoreaMexicoOther - Asia PacificOther - Europe and AfricaOther - North and South AmericaRussiaTaiwanUnited KingdomUnited StatesAI ApplianceAI Software/SaaSEnergy and UtilitiesImage/Object Detection/Recognition/ClassificationPredictive maintenance and analyticsCSP - Amazon Web ServicesCSP - Google CloudCSP - Microsoft AzureOn-premise (Private Cloud, Other)Intel® Xeon® Scalable ProcessorIntel® Core™ ProcessorIntel® Movidius™ Vision Processing Units (VPU)Intel® FPGAIntel® Distribution of OpenVINO™ Toolkit powered by oneAPIIntel® Optimization for TensorFlow*Models can be trained - requires labeled dataWindowsLinux SSDDeep LearningMachine Learning