Efficient neuro-fuzzy damage severity estimation in an experimental wind turbine blade using the Fukunaga-Koontz transform of vibration signal correlations

Potential energy outputs of wind turbines (WTs) are subject to continuous enhancements due to increasing demands for carbon neutral energy. The use of novel composite materials facilitates erections of ever larger WTs, which capture more energy by using longer WT blades (WTBs) with reduced weight. However, higher flexibilities and lower buckling capacities of these WTBs adversely affect long-term safety and reliability of WTs and, with it, energy production costs. This can be counteracted with the help of efficient structural health monitoring (SHM). The present study shows a novel methodology for vibration-based structural damage detection and severity estimation in WTBs. First, correlations of vibrational response signals are extracted as initial damage sensitive features (DSFs). Second, the Fukunaga-Koontz transform, an extension of the better-known Karhunen-Loéve expansion, is applied for extracting secondary DSFs with improved damage sensitivities. Third, univariate rankings of both DSFs are separately created with respect to the area under the receiver operating characteristic curve. Then, structural damage detection and severity estimation is performed with the help of hierarchical adaptive neuro-fuzzy inference systems, where the hierarchical structure allows accounting for the ranking information. The method is applied to laboratory experimental data from a small WTB exited by an air stream produced by a household fan. Damage severity estimation is studied by attaching different small masses as non-destructive damage scenarios. The results demonstrate that the proposed methodology enables to detect and estimate accurately the severity of the simulated damage. Furthermore, the advantages of using transformed DSFs are shown. This is promising for future developments of vibration-based SHM to facilitate improved safety and reliability of WTs at lower costs.