AI-driven method helps improve quality assurance for wind turbines

An international collaboration between EPFL and the University of Glasgow has led to an advanced machine-learning algorithm to effectively detect concealed manufacturing defects in wind turbine composite blades - before turbines are put into service. Faulty wind turbine blades can incur huge costs for the companies that operate them, especially if the defects go unnoticed until it's too late. That's why quality assurance is such a strategic issue for global wind-turbine manufacturers. Today, quality inspections are limited to surface inspection of limited areas as these composite structures roll off the production line. But under a new approach co-created by EPFL and University of Glasgow researchers, inspection engineers can use a new patented radar technology, combined with an AI assistant, to detect possible anomalies beneath the surface. This approach has many advantages: it's non-destructive, non-contact, supports agile and rapid data acquisition and analysis, and requires very little power to operate. The research has just been published in Elsevier Mechanical Systems and Signal Processing (MSSP).