Imagification Technology and Deep Learning Accelerating Defect Detection in Non-Destructive Testing for Wind Turbine Blades

Wind power generation is a fast-growing type of renewable energy that is highly recognized for its potential. Turbine blades are a crucial part of power generation systems, and they bear substantial loads in operation. Therefore, quality inspection standards during production are extremely strict in order to meet critical to quality (CTQ) requirements. Quality inspections include ultrasonic non-destructive testing (NDT) methods, for instance, which involve an engineer's detailed examination of ultrasonic testing (UT) scan image data covering a length of approximately 75 meters to identify possibly defective parts of a few centimeters. This is a time-consuming process, and human error is always a risk. Fujitsu Laboratories of Europe Ltd. has developed a unique technology, Imagification, and combined it with an image-recognition deep learning engine to develop a system for automating defect detection, helping to reduce the inspection load and prevent human errors. We have further developed the system by digitizing and integrating customer knowledge and expertise in blade structure, ultrasonic NDT procedures and know-how in defect characterization to build a new automated quality inspection solution. This paper explains the newly developed defect detection system and describes the process of digital co-creation with customers to realize commercial applications of the technology.