There is a need to better understand the effect of temperature changes on the response of ultrasonic guided-wave pitch-catch systems used for structural health monitoring. A model is proposed to account for all relevant temperature-dependent parameters of a pitch-catch system on an isotropic plate, including the actuator-plate and plate-sensor interactions through shear-lag behavior, the piezoelectric and dielectric permittivity properties of the transducers, and the Lamb wave dispersion properties of the substrate plate. The model is used to predict the S0 and A0 response spectra in aluminum plates for the temperature range of −40–+60°C, which accounts for normal aircraft operations. The transducers examined are monolithic PZT-5A [PZT denotes Pb(Zr–Ti)O3] patches and flexible macrofiber composite type P1 patches. The study shows substantial changes in Lamb wave amplitude response caused solely by temperature excursions. It is also shown that, for the transducers considered, the response amplitude changes follow two opposite trends below and above ambient temperature (20°C), respectively. These results can provide a basis for the compensation of temperature effects in guided-wave damage detection systems.

This paper deals with the propagation of ultrasonic guided waves in adhesively bonded lap–shear joints. The topic is relevant to bond inspection by ultrasonic testing. Specifically, the propagation of the lowest-order, antisymmetric $a_0$ mode through the joint is examined. An important aspect is the mode conversion at the boundaries between the single-plate adherents and the multilayer overlap. The $a_0$ strength of transmission is studied for three different bond states in aluminum joints, namely a fully cured adhesive bond, a poorly cured adhesive bond, and a slip bond. Theoretical predictions indicate that the dispersive behavior of the guided waves in the multilayer overlap is highly dependent on bond state. Experimental tests are conducted in lap–shear joints by a hybrid, broadband laser/air-coupled ultrasonic setup in a through-transmission configuration. The Gabor wavelet transform is employed to extract energy transmission coefficients in the 100 kHz 1.4 MHz range for the three different bond states examined. The cross-sectional mode shapes of the guided waves are shown to have a substantial role in the energy transfer through the joint.

The detection and localization of structural damage in a stiffened skin-to-stringer composite panel typical of modern aircraft construction can be addressed by ultrasonic-guided wave transducer arrays. However, the geometrical and material complexities of this part make it quite difficult to utilize physics-based concepts of wave scattering. A data-driven deep learning (DL) approach based on the convolutional neural network (CNN) is used instead for this application. The DL technique automatically selects the most sensitive wave features based on the learned training data. In addition, the generalization abilities of the network allow for detection of damage that can be different from the training scenarios. This article describes a specific 1D-CNN algorithm that has been designed for this application, and it demonstrates its ability to image damage in key regions of the stiffened composite test panel, particularly the skin region, the stringer’s flange region, and the stringer’s cap region. Covering the stringer’s regions from guided wave transducers located solely on the skin is a particularly attractive feature of the proposed SHM approach for this kind of complex structure.

Recent theoretical and experimental studies in a wide range of applications have demonstrated that Green’s functions (impulse responses) can be extracted from cross-correlation of diffuse fields using only passive sensors. This letter demonstrates the passive-only reconstruction of coherent Lamb waves (dc–500kHz) in an aluminum plate of thickness comparable to aircraft fuselage and wing panels. It is further shown that the passively reconstructed waves are sensitive to the presence of damage in the plate as it would be expected in a typical “active” guided wave test. This proof-of-principle study suggests the potential for a structural health monitoring method for aircraft panels based on passive ultrasound imaging reconstructed from diffuse fields.

Multi-wire strands are widely used in civil structures as tensioning members in prestressed concrete, cable-stayed and suspension bridges. In recent years, various researchers including the authors have investigated guided ultrasonic waves as a tool for monitoring the health of the strands. Magnetostrictive ultrasonic transducers have been successfully used to excite and detect the guided waves in these components. This study improves the general guided-wave technique with magnetostrictive transducers for the detection and sizing of defects in strands. The improvement consists of extracting sensitive and robust features of the wave signals, and then using these features to construct a damage index (D.I.). The proposed D.I. is unaffected by accidental variations in the excitation power or by changes in the electromechanical coupling efficiency of the transducers. The specific defect under investigation is a notch cut at varying depths in a strand that is subjected to a typical operational load. Features extracted after discrete wavelet processing of the wave signals result in a D.I. that is robust against noise and is linearly related to the notch depth in a logarithmic scale. In particular, the variance of the signal’s wavelet coefficients results in the largest sensitivity to notch depth.

This paper casts pipe inspection by ultrasonic guided waves in a feature extraction and automatic classification framework. The specific defect under investigation is a small notch cut in an ASTM-A53-F steel pipe at depths ranging from 1% to 17% of the pipe cross-sectional area. A semi-analytical finite element method is first used to model wave propagation in the pipe. In the experiment, reflection measurements are taken and six features are extracted from the discrete wavelet decomposition of the raw signals and from the Hilbert and Fourier transforms of the reconstructed signals. A six-dimensional damage index is then constructed, and it is fed to an artificial neural network that classifies the size and the location of the notch. Overall, the wavelet-based multifeature analysis demonstrates good classification performance and robustness against noise and changes in some of the operating parameters.