Open AccessStructural health monitoring for delamination detection and location in wind turbine blades employing guided waves
Author(s) -
Gómez Muñoz Carlos Quiterio,
García Marquez Fausto Pedro,
Hernandez Crespo Borja,
Makaya Kena
Publication year - 2019
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.2316
Subject(s) - delamination (geology) , structural health monitoring , wavelet , wind power , turbine , condition monitoring , wavelet transform , propeller , fault (geology) , turbine blade , nacelle , engineering , structural engineering , daubechies wavelet , fault detection and isolation , acoustics , renewable energy , automotive engineering , marine engineering , computer science , mechanical engineering , artificial intelligence , geology , wavelet packet decomposition , electrical engineering , seismology , physics , subduction , tectonics , actuator
Wind power is becoming one of the most important renewable energies in the world. The reduction in operating and maintenance costs of the wind turbines has been identified as one of the biggest challenges to establish this energy as an alternative to fossil fuels. Predictive maintenance can detect a potential failure at an early stage reducing operating costs. Structural health monitoring together with non‐destructive techniques are an effective method to detect incipient delamination in wind turbine blades. Ultrasonic guided waves offer possibilities to inspect delamination and disunion between layers in composite structures. Delamination results in a concentration of tensions in certain areas near the fault, which can propagate and create the total break of the blade. This paper presents a new approach for disunity detection between layers comparing two real blades, also new in the literature, one of them built with three disbonds introduced in its manufacturing process. The signals are denoised by Daubechies wavelet transform. The threshold for the denoising is obtained by a wavelet coefficients selection rule using the Birgé‐Massart penalization method. The signals were normalized and their envelopes were obtained by Hilbert transform. Finally, a pattern recognition based on correlations was applied.