Wavelet packet decomposition

Tectonics

Engineering

Fault detection and isolation

Daubechies wavelet

Aerospace engineering

Turbine blade

Marine engineering

Actuator

Nacelle

Computer science

Subduction

Acoustics

Fault (geology)

Seismology

Condition monitoring

Geology

Structural health monitoring

Wavelet transform

Mechanical engineering

Physics

Propeller

Renewable energy

Automotive engineering

Environmental science

Artificial intelligence

Turbine

Wind power

Structural engineering

Delamination (geology)

Wavelet

Electrical engineering

Wind Energy

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.

Structural health monitoring for delamination detection and location in wind turbine blades employing guided waves