Structural Damage Detection based on Aerodynamic Pressure Measurements

Abstract

Recent designs of large wind turbine blades have become increasingly flexible, and thus require cost-efficient monitoring solutions. For this purpose, we propose to use aerodynamic pressure measurements obtained with a novel, non-intrusive and economical sensing system, called Aerosense. In this work, we investigate the potential use of aerodynamic pressure measurements around a 2D airfoil for structural damage detection on elastic and aerodynamically loaded structures. Our investigation is based on a series of experiments conducted in an open wind tunnel using a NACA 633418 airfoil. In our setup, the airfoil is mounted on a vertically vibrating cantilever beam. Structural damage in form of a crack is introduced by stepwise sawing the cantilever beam at its support. The aerodynamic pressure distribution on the airfoil is measured with the Aerosense system under numerous varying inflow conditions and structural configurations. We analyze the dynamic behavior of the structural system based on reference acceleration measurements and compare it to finite element simulations. Based on that knowledge, we propose an algorithm using convolutional neural networks to detect damage and assess its severity from the pressure measurement data. The application of this algorithm to our experimentally acquired data demonstrates that aerodynamic pressure measurements on airfoils can serve for damage detection and severity