Online and offline monitoring of composite bolted joints under tensile load were investigated using piezoelectric transducers. The relationships between Lamb wave signals, pre-tightening force, the applied tensile load, as well as the failure modes were investigated. Results indicated that S0/A0 wave amplitudes decrease with the increasing of load. Relationships between damage features and S0/A0 mode were built based on the finite element (FE) simulation and experimental results. The possibility of application of Lamb wave-based structure health monitoring in bolted joint-like composite structures was thus achieved.

Localization of fatigue cracks imposes immense significance to ensure the health of the engineering structures and prevent further catastrophic accidents. The nonlinear ultrasonic waves, especially the nonlinear Lamb waves, have been increasingly studied and employed for identifying micro-damages that are usually invisible to traditional linear ultrasonic waves. However, it remains a challenge to locate the fatigue cracks using nonlinear Lamb waves owing to the enormous difficulties in decoding location information from acoustic nonlinearity. Motivated by this, this work presents a data-driven method for precise location of fatigue crack using nonlinear Lamb waves. A 1D-Attention-convolutional neural network is developed to correlate the fatigue crack location with the wavelet coefficients at the second harmonic frequency of Lamb wave signals. The introduction of the Attention layer enables the models to pay more attention to the desired nonlinear features which dominates locating the fatigue crack. In particular, a convenient dataset creation scheme guided by the relative value label is proposed to generate sufficient data commonly required for deep learning approach. In addition, a lightweight single-excite-multiple-receive signal acquisition method is adopted instead of full-matrix capture method used in the traditional research, which highly improves detection efficiency. Numerical simulation and experimental validation manifest that the trained network can be used to establish the complex mapping between the nonlinear ultrasonic signals and the fatigue crack location features, so as to locate barely visible fatigue cracks. Our work provides a promising and practical way to facilitate nonlinear Lamb waves to accurately locate fatigue cracks in large-scale plate-like structures.

Resilient wheel is a multi-layer composite structure with discontinuous impedance, the internal structure is prone to damage due to repeated interaction. It is a difficult point for damage detection and imaging of the resilient wheel. This paper proposes a phased array ultrasonic imaging method based on FDMAS-TFM, and high-resolution imaging for defects in the different positions of the resilient wheel is investigated. This method introduces a pairwise multiplication between the delay and summation on the ultrasonic signals, which utilizes the spatial coherence of signals, then the second harmonic component is filtered out for ultrasonic imaging. Finite element simulation and experimental results indicate that the imaging performance of FDMAS-TFM is better than that of the total focusing method (TFM), mainly embodied in higher imaging resolution and stronger noise rejection. For imaging defect inside the wheel rim, the FDMAS-TFM increases signal-to-noise ratio by up to 21.55dB compared with the TFM. For imaging defect at the joint between wheel rim and rubber block, the FDMAS-TFM reduces array performance index by at least 36% compared with the TFM. The results show practical application capability of the proposed method in the resilient wheel.

The total focusing method (TFM) attracts much interest because of high image resolution and large dynamic range. However, the signal-to-noise ratio (SNR) of TFM is relatively low due to single-element emission. Especially in highly attenuating materials, the signal amplitude may be reduced with depth to a level lower than the electronic noise introduced by the signal acquisition system. Golay-coded excitation was introduced to overcome this problem, however, the double-excitations reduce the time resolution and may lead to negative decoding performance due to the relative change of scanning positions. In this work, the AB code conversion factor was used to realize the TFM imaging based on Golay-coded single-excitation which has a good performance as traditional Golay-coded excitation. A series of simulations and experiments were conducted to compare performance of TFM using un-coded excitation, Golay-coded excitation and modified Golay-coded excitation. TFM using modified Golay-coded excitation could overcome the disadvantages of double-excitation, and has approximately the same SNR gain as TFM using traditional one.