Automated Modal Analysis of an airfoil

Abstract

Nowadays with the urge to obtain renewable energy, the pursuit for larger wind turbines is growing rapidly. Simultaneously, the locations are becoming more remote. As a consequence the complications bound to maintenance are building up. In short, the need for a Structural Health Monitoring (SHM) system is increasing. For the SHM system to detect a damage as early as possible it is necessary to reduce the uncertainty bounds of the Damage Sensitivity Features (DSF). Several system identification methods, both non-parametric and parametric, for extraction of the Modal Parameters (MP) of a Finite Element (FE) representation of a laboratory-scaled wind turbine blade are analyzed in terms of the associated bias and the variance. The bias and the variance of each system identification method are estimated using extensive Monte Carlo simulations. The result hereof showed that the Output Only (OO) based AutoRegressive models had high accuracy in terms of less bias and variance. Similar accuracy was seen for MP extraction with the use of the Power Spectral Density (PSD) obtained from the outputs.