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. Nondestructive ultrasonic test techniques are commonly applied in their predictive maintenance. This work proposes the use of novelty detection methods combined with nondestructive ultrasound testing to identify structural problems in wind turbine blades. Ultrasound signals are preprocessed using both, wavelet denoising and principal component analysis. Novelty detection deals with the one-class classification problem, when only the normal condition signatures are required for the classification system design. For the nondestructive test of turbine blades, this is an interesting paradigm because it is not always possible to obtain test samples from all of the existing flaw conditions. Our experimental results indicate the efficiency of the proposed method.

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. This characterizes the carburization phenomenon, which modifies magnetic properties, reduces mechanical resistance and may lead to structural rupture. For safe operation it is required to frequently determine carburizing level in pyrolysis furnace pipes. This is traditionally performed manually using magnetic evaluation. This work proposes a novel procedure for carburizing level estimation using ultrasonic evaluation associated to signal processing and machine learning techniques. Experimental data from pulse-echo ultrasonic tests performed in HP steel pipes are used. Discrete Fourier transform was applied for feature extraction and different classification systems (neural networks, k-nearest neighbors and decision trees) are applied and compared in terms of carburizing level identification efficiency.