A New Vision-Based Method Using Deep Learning for Damage Inspection in Wind Turbine Blades

Abstract

Wind turbines are having great impact in the field of clean energies. However, there is a need to improve these technologies in various aspects, such as: maintenance, energy storage, cases of overload or mechanical failure. In maintenance, they constantly suffer of damage in blades typically to be in the open air and in constant operation. The most well-known damages in blades are identified as: impact of rays, wearing, fractures by cutting forces, freezing, among others. Because of all these factors, it is necessary to develop a predictive technique to help us to do the inspections of the blades in a safer and more effective way than manual inspection. In that sense, this paper introduces a deep learning vision-based approach to automatically analyze each part of the face of the blade, capable of making the detection of certain faults (impact of rays, wear and fractures). In addition, we present a proof-of-concept using a robot to automatically detect failures in wind turbine blades. Experimental results validate our vision system.