This work proposes a novel hybrid signal processing technique to extract information on disbond-type defects from a single B-scan in the process of non-destructive testing (NDT) of glass fiber reinforced plastic (GFRP) material using ultrasonic guided waves (GW). The selected GFRP sample has been a segment of wind turbine blade, which possessed an aerodynamic shape. Two disbond type defects having diameters of 15 mm and 25 mm were artificially constructed on its trailing edge. The experiment has been performed using the low-frequency ultrasonic system developed at the Ultrasound Institute of Kaunas University of Technology and only one side of the sample was accessed. A special configuration of the transmitting and receiving transducers fixed on a movable panel with a separation distance of 50 mm was proposed for recording the ultrasonic guided wave signals at each one-millimeter step along the scanning distance up to 500 mm. Finally, the hybrid signal processing technique comprising the valuable features of the three most promising signal processing techniques: cross-correlation, wavelet transform, and Hilbert–Huang transform has been applied to the received signals for the extraction of defects information from a single B-scan image. The wavelet transform and cross-correlation techniques have been combined in order to extract the approximated size and location of the defects and measurements of time delays. Thereafter, Hilbert–Huang transform has been applied to the wavelet transformed signal to compare the variation of instantaneous frequencies and instantaneous amplitudes of the defect-free and defective signals.

In this paper, a novel 2D analytical model based on the Huygens’s principle of wave propagation is proposed in order to predict the directivity patterns of contact type ultrasonic transducers in the generation of guided waves (GWs). The developed model is able to estimate the directivity patterns at any distance, at any excitation frequency and for any configuration and shape of the transducers with prior information of phase dispersive characteristics of the guided wave modes and the behavior of transducer. This, in turn, facilitates to choose the appropriate transducer or arrays of transducers, suitable guided wave modes and excitation frequency for the nondestructive testing (NDT) and structural health monitoring (SHM) applications. The model is demonstrated for P1-type macro-fiber composite (MFC) transducer glued on a 2 mm thick aluminum (Al) alloy plate. The directivity patterns of MFC transducer in the generation of fundamental guided Lamb modes (the S0 and A0) and shear horizontal mode (the SH0) are successfully obtained at 80 kHz, 5-period excitation signal. The results are verified using 3D finite element (FE) modelling and experimental investigation. The results obtained using the proposed model shows the good agreement with those obtained using numerical simulations and experimental analysis. The calculation time using the analytical model was significantly shorter as compared to the time spent in experimental analysis and FE numerical modelling.

In this paper, the disbond-type defect presented on glass fiber reinforced plastic material is analyzed by refining the guided Lamb wave signals. A segment of wind turbine blade is considered as a test sample. The low-frequency ultrasonic measurement system is used for the non-destructive testing of the test sample using guided waves. The P-1 type macro-fiber composite transducer as a transmitter and contact-type piezoceramic transducer as a receiver are used for the testing of a sample. The disbond type defect having a diameter of 81 mm is detected from the experimental results. To improve the accuracy in locating and sizing the defects and estimation of the time of flight and phase velocity of ultrasonic guided waves in defective region, signal processing algorithm is developed by utilizing the promising properties of various ultrasonic signal processing techniques such as wavelet transform, amplitude detection, two-dimensional Fast-Fourier transform, Hilbert transform and variational mode decomposition. The discrete wavelet transform is used to denoise the guided wave signals and then, the size and location of defects are estimated by amplitude detection. The reflected wave signals from the opposite edge of the sample are removed by applying the two-dimensional Fast-Fourier transform to the experimental B-scan signal. Afterwards, variational mode decomposition and Hilbert transform are used for the phase velocity and time-delay estimation by comparing the instantaneous amplitudes of the defective and defect-free signal. The validation and the demonstration of reproducibility of the algorithm is performed by extracting the features of a 51 mm defect from another experimental B-scan.

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Dermatoscopy, high-frequency ultrasonography (HFUS) and spectrophotometry are promising quantitative imaging techniques for the investigation and diagnostics of cutaneous melanocytic tumors. In this paper, we propose the hybrid technique and automatic prognostic models by combining the quantitative image parameters of ultrasonic B-scan images, dermatoscopic and spectrophotometric images (melanin, blood and collagen) to increase accuracy in the diagnostics of cutaneous melanoma. The extracted sets of various quantitative parameters and features of dermatoscopic, ultrasonic and spectrometric images were used to develop the four different classification models: logistic regression (LR), linear discriminant analysis (LDA), support vector machine (SVM) and Naive Bayes. The results were compared to the combination of only two techniques out of three. The reliable differentiation between melanocytic naevus and melanoma were achieved by the proposed technique. The accuracy of more than 90% was estimated in the case of LR, LDA and SVM by the proposed method.