Driving Sustainable Energy with HCL Wind Turbine Defect Detection Solution – Intel on AI – Episode 59

In this Intel on AI podcast episode: As wind turbines proliferate and grow in size and complexity, the biggest challenge to Wind Energy is the high cost of Operations and Maintenance. Manual inspection and maintenance is dangerous and expensive. With the advent of drones, gathering maintenance footage has become much easier, but without the use of AI technology, inspecting tons of footage and data is time consuming, expensive, ineffective. One defect can potentially incapacitate an entire turbine, however automation of maintenance can significantly improve the value and cost of wind energy. Alberto Gutierrez Ph.D., Chief Data Scientist at HCL America, joins the Intel on AI podcast to talk about HCL's deep learning (DL) based wind turbine defect detection solution and how it is helping to drive sustainable energy today.

He illustrates how HCL's solution enables wind energy operators to utilize drone technology to capture images of turbines and use deep neural network (DNN) computer vision algorithm to find potential defects in those turbines. Some of the defects that are often detected include visible defects on blade surfaces like missing teeth in VG (Vortex Generator) or panel and blade edge corrosion. Alberto describes how using AI and drones to address this workload is ultimately a safer and less expensive option that helps make wind energy cheaper and more attractive as an alternative, clean energy source. He also discusses how HCL has collaborated closely with the Intel AI Builders program to optimize their solution's DL model using the Intel Distribution of OpenVINO toolkit to process video stream, image segmentation and object detection.

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