Boston-based AirFusion, a developer of damage-detection and risk-prediction software powered by artificial intelligence (AI), has announced AirFusion Wind, a cloud-based workflow and AI-based analysis platform to identify and classify wind turbine asset damage.
AirFusion Wind monitors turbine conditions, identifies damage and asset degradation, and streamlines inspection workflows and reporting. The platform delivers fast, accurate analysis of wind turbine inspection data – enabling proactive, predictive maintenance that significantly reduces the risk of catastrophic failure, excessive downtime and performance-based revenue loss, the company says. AirFusion Wind rapidly transforms pixel-based inspection imagery from drones, ground-based sensors and other image-capture tools.
AirFusion says its patent-pending sensor fusion technology – in combination with a convolutional neural network (cnn) AI technology core – leverages terabytes of specialized images in a unique training set built around vertical-specific heuristics and deep learning techniques from wind experts around the world. The self-learning AI system continually ingests new sensor images and related data to optimize overall accuracy.
“Only AI-based solutions will be powerful enough to handle the demand, scale and complexities of autonomous wind turbine inspections,” comments Dennis Chateauneuf, president and CEO of AirFusion. “AirFusion Wind’s unique technology combination of image recognition, patent-pending sensor fusion technology and AI provides the consistency to provide highly accurate inspections and the scalability required by our customers. With AirFusion Wind, inspection data analysis is reduced from hours to minutes, enabling prognostics and prescriptive maintenance that dramatically reduce operational costs.”
