GE Vernova Is Deploying AI-Enabled Machines to Boost Wind Turbine Blade Quality
GE Vernova, under the leadership of CEO Vic Abate, is intensifying its focus on wind energy to meet global energy demands and accelerate decarbonization. Currently providing 7% of electricity globally, wind power aims to reach 25%, requiring a substantial increase in turbine production.
Each wind turbine undergoes a meticulous manufacturing process involving steel tower construction, nacelle fiberglass molding, and blade assembly, with each blade taking about 2,000 labor hours to handcraft from fiberglass and balsa wood. To ensure quality, GE Vernova integrates robotics and AI for advanced inspections, crucial for detecting defects early and minimizing operational issues post-installation.
The use of AI extends to raw material inspections and the entire manufacturing process, ensuring consistent quality across global production sites. With blades weighing 20 tons and spanning 80 meters, precision is paramount; even minor deviations can impact durability and efficiency significantly.
GE Vernova’s innovations include 360-degree digital cameras for detailed interior inspections, enhancing the accuracy and scalability of defect detection. This technology supports their mission to produce reliable turbines crucial for the world’s renewable energy transition.
GE Vernova's next innovation involved harnessing computer vision, a branch of AI focused on teaching computers to recognize visual information in images. Initially, human inspectors compiled a detailed list of deviations they typically sought out during blade inspections. This information served as the foundation for training AI algorithms through a rigorous process.
Veronica Barner explains, "We're using annotated images to train a series of AI algorithms capable of analyzing images and autonomously flagging potential anomalies." After extensive training on tens of thousands of annotated images, the AI quickly mastered the task of identifying deviations with high accuracy. The algorithms automatically document any flaws in a digital tool, allowing human technicians to review and address issues before the blade is shipped to turbine operators.
Barner emphasizes the efficiency gains: "Deploying this to our factory floors enriches the feedback loop. Within minutes, our teams have critical, timely insights into the blades they're working on, akin to a prenatal ultrasound."
A major challenge was accessing the blade's interior for inspection. GE Vernova solved this by deploying a fleet of robotic crawlers operated remotely. These crawlers, approximately the size of a two-foot model car, can navigate the entire inner surface of the blade in just 30 minutes. This capability is crucial as approximately 50% of the blade's inner surface is inaccessible to human inspectors.
These advancements underscore GE Vernova's commitment to leveraging cutting-edge technology to enhance turbine quality and reliability, crucial for supporting the global shift towards renewable energy.
