Ultrasound-based identification of damage in wind turbine blades using novelty detection.

Among the renewable energy sources, wind power generation presents competitive costs and high installation potential in many countries. Ensuring the integrity of the generation equipment plays an important role for reliable energy production. Therefore, nondestructive test procedures are required, especially for turbine blades, which are subject to severe operational conditions due to phenomena such as lightning strikes, mechanical stress, humidity and corrosion.

Segmented analysis of time-of-flight diffraction ultrasound for flaw detection in welded steel plates using extreme learning machines.

This work investigates the application of extreme learning machine, a fast training neural network model, for an ultrasound nondestructive evaluation decision support system. A novel segmented analysis of time-of-flight diffraction ultrasound signals is proposed in order to produce high flaw detection efficiency and low computational requirements, making it possible to be used in embedded applications. The frequency contents of TOFD signals temporal segments, estimated using the discrete Fourier transform, were used to feed the classification system.

Carburization level identification in industrial HP pipes using ultrasonic evaluation and machine learning.

Ultrasound nondestructive testing is commonly applied in industry to guarantee structural integrity. HP steel pyrolysis furnaces are used in petrochemical industry for lightweight hydrocarbon production. HP steel chromium content may be reduced in high-temperatures due to carbon diffusion.

Scaling properties of randomly folded plastic sheets.

We study the scaling properties of randomly folded aluminum sheets of different thicknesses h and widths L . We found that the fractal dimension D=2.30+/-0.