Under complex underground operating conditions,mechanical noise generated by belt friction and coal flow impacts,airflow-induced disturbance noise,and coupled noise from multiple devices are superimposed,causing fault-related acoustic signatures of idlers to be easily masked by environmental noise.Meanwhile,the acquisition of abnormal idler samples is difficult and annotation costs are high,making traditional supervised learning-based idler abnormal condition detection methods hard to generalize effectively.To address these issues,an unsupervised idler abnormal condition detection method based on Multi-Granularity Attention Autoencoder(MG-AAE)was proposed,which used only normal-condition idler sounds for model training and required no fault labels.A multi-granularity composite acoustic feature composed of Mel spectrograms and Mel-Frequency Cepstral Coefficients(MFCCs)was constructed to jointly capture energy contours and fine-grained acoustic signatures.A Gaussian Difference Pyramid(GDP)and a Multi-Head Attention(MHA)mechanism were introduced into the encoder to perform multi-scale modeling and adaptive weighted fusion,thereby suppressing steady background noise and highlighting key fault-related frequency bands.A multi-dimensional reconstruction mean-square error was used as the anomaly criterion to achieve automatic identification of idler abnormal conditions.Experimental results showed that,when trained using only normal samples,the MG-AAE model demonstrated excellent performance in cross-device and real-world operating conditions.Evaluation on four typical device categories in the MIMII dataset showed that,under a strong noise condition of 0 dB,the average area under curve(AUC)and local AUC(pAUC).f the MG-AAE model reached 84.2％and 70.4％,respectively,representing improvements of 7.3％and 5.6％over the Autoencoder model.On real idler data,the AUC reached 95.47％,and the reconstruction error of abnormal samples was approximately 1.40 times that of normal samples.These results indicate that the proposed method has good cross-device generalization and a low false alarm rate,and provides effective technical support for abnormal condition detection of idlers in coal mine belt conveyor systems.

In-service drill pipes, operating in deep wells, function to transfer the output torque and power from the drilling rig at the surface to drill bit at the bottom of the well. These pipes consist of a series of thread-connected single drill pipes. The threaded connections between adjacent drill pipes represent the weakest regions within the drill string. Consequently, inspecting these threaded connections is crucial for ensuring the safe operation of drilling rigs. Currently, the most common inspection methods can only detect damage on the internal or external threads of an individual, unconnected drill pipe. However, they are not applicable for inspecting assembled threaded connections. This study proposes the use of ultrasonic guided waves (UGWs) for inspecting threaded connections in drill pipes, enabling on-assembly inspection. In the proposed method, UGWs are excited in the pipe body of one drill pipe, transmitted across the threaded connection, and received in the pipe body of the adjacent drill pipe. A finite element model is constructed to simulate UGW propagation through both normal and damaged threaded connections. Furthermore, experimental platforms are established for GUW inspection of threaded connections. Experiments were conducted on specimens with normal and damaged connections. Both numerical and experimental results demonstrate that UGWs can effectively detect and locate damage on either the internal or external threads within a threaded connection. It is concluded that the UGW technique offers a promising approach for threaded connection inspection, facilitating the inspection of assembled drill pipe connections.

Carbon fiber-reinforced polymer (CFRP) is widely used in aerospace and other industrial fields due to its high strength-to-weight ratio, excellent temperature and corrosion resistance. However, manufacturing-induced delamination defects can seriously compromise structural integrity, making nondestructive evaluation (NDE) essential. This paper proposes an improved defect identification method that combines an enhanced elliptical localization algorithm with Lamb wave-based probabilistic imaging for promising delamination identification in CFRP laminates under the tested conditions. First, the dispersion curves of Lamb waves are calculated using the 1D-GLL-SAFE method in SAFEDC software, and S0/A0 modal wave velocity radar maps are generated to visualize directional dependence in the anisotropic structure. Finite element models with controlled delamination defects are then established in ABAQUS to simulate Lamb wave propagation and defect interaction. Simulation results show absolute localization errors of 3.91 mm and 3.61 mm, with relative errors of 1.85

To address the challenges of wave packet broadening and signal distortion caused by multimodality and dispersion effects in Lamb wave-based damage detection for carbon fibre-reinforced polymer (CFRP) laminates, this paper proposes an imaging method that combines multimodal dispersion compensation and compressive sensing (CS). First, the Rayleigh-Lamb equation is solved using the T300/5028 material parameters to establish the dispersion characteristic model. A baseline-free multimodal dispersion compensation technique is developed for performing frequency-domain phase correction and time-domain signal reconstruction, allowing accurate estimation of the compensation distance between each sensor and the damage. Subsequently, a CS-based imaging algorithm that fuses delay-and-sum (DAS) and sparse reconstruction is proposed and verified using finite-element simulations and experimental tests. The results show maximum localisation errors of 2.03 +/- 0.05 mm in simulations and 2.31 +/- 0.56 mm in experiments. This work provides theoretical insights and experimental support for the high-precise identification of delamination damage in anisotropic composite materials.

Threaded pipe structures are critical components in equipment used in various fields. The threaded sections are prone to induce defects due to stress concentration, threatening the safe operation of the equipment. Consequently, the inspection of these structures is essential. While the ultrasonic guided wave (UGW) method has been applied to inspect threaded pipes, the influence of thread parameters on UGW propagation characteristics is yet to be examined. This study first analyzed the dispersion characteristics of UGWs. It reveals that the group velocity dispersion curve of the L(0,2) mode initially increases and then decreases over the frequency range of 60–140 kHz. The group velocity is higher in trapezoidal threads than in rectangular threads. Dispersion curves for different thread heights exhibit a crossover near 85 kHz. Dispersion curves for different pitches intersect around 83 kHz. Second, the effect of thread parameter variations on the reflection characteristics was investigated. It was found that the trapezoidal thread exhibits a higher reflection coefficient than the rectangular thread. The reflection coefficient increases with the thread height and decreases with pitch. Third, the influence of thread parameters on defect detection sensitivity was examined. Results demonstrate that the L(0,2) mode offers high sensitivity for defect detection in threaded pipes featuring trapezoidal threads, a thread height of 1 mm, and a pitch of 4.5 mm. Finally, the effectiveness of the L(0,2) mode in detecting defects of varying depths within threaded pipes was validated. This research provides a novel method for the inspection of threaded pipe structures.

Wind turbine blades are susceptible to low-velocity impacts, such as hail and bird strikes, which can induce hidden damage including matrix cracking, delamination, and fiber breakage. Conventional multi-sensor localization methods based on time difference of arrival require sensor arrays, wave-velocity calibration, and synchronized multi-channel acquisition. In composite blade spars, first-arrival time picking and effective wave-velocity estimation are further complicated by material anisotropy, thickness variation, boundary reflections, attenuation, and dispersive wave propagation. To reduce these dependencies, this study develops a single-sensor acoustic emission (AE)-based method for simultaneous impact source localization and impact intensity condition identification. Rather than explicitly estimating arrival times or wave velocities, the method treats the single-channel AE response as a path and input sensitive acoustic signature, in which impact input, propagation path, and structural non-uniformity are inherently coupled. A multi-domain representation is constructed from single-channel AE responses by integrating complementary time-domain, spectral, and band-energy features, and is then used for CNN-BiLSTM-based impact identification. Experiments were conducted on a wind turbine blade under multiple impact locations and impact-input conditions. The proposed method achieved 99.870% accuracy in grid level impact source localization and 95.964%-96.745% accuracy in impact intensity condition identification. These results indicate that single-sensor AE responses contain discriminative information related to both impact location and input condition, providing a simplified and effective approach for impact monitoring of wind turbine blades.

The Acoustic Influence Map (AIM) shows the acoustic energy distribution of total focusing images at different spatial positions. To address the computational complexity and low efficiency of existing AIMs, a deep-conditional diffusion model is proposed. An improved U-Net is adopted as the backbone network, and a four-level encoder-decoder structure is constructed. The input channels are expanded to 34, and discrete depths are converted into 32-channel feature maps to match the image dimensions by a learnable embedding layer. The mask-depth embedding dual-condition input mechanism is realised by a single-channel mask image and a noise image. The generated images are evaluated using the Structural Similarity Index (SSIM) and Pearson Correlation Coefficient (CC). The range of SSIM value is from 0.86 to 0.93, indicating that the model can effectively preserve the details of the images. The range of CC value is from 0.68 to 0.86, demonstrating that the model is able to reconstruct the geometric structure of total focusing images. The normalised amplitudes predicted by the AIM are compared with the total focusing images of holes. The results show a correlation coefficient of 0.96 between the two datasets, indicating a strong positive correlation between the predicted values of the acoustic influence map and the actual imaging results.

The special eddy field of mesoscale vortices plays an important role in the global shipping process. The statistical morphology of mesoscale vortices observed via global satellites and the numerical simulation of the ocean are applied to the simulation of computational fluid dynamics, which can more truly reflect the influence of mesoscale vortices on the motion characteristics of underwater vehicles. In this paper, the ALE (Arbitrary Lagrangian–Eulerian) finite element method is used to simulate the random vortex of a submarine in three dimensions (horizontal x, vertical z, height y) and establish quantitative submarine movement characteristics. Our results show that with an increase in mesoscale vortex strength, the effects on the submarine’s speed and displacement increase, but the overall effect is still limited. In the 300 m transmission simulation, the velocity effect is within ±2 m/s, and the displacement effect is within 4 m. The simulation results can be applied to the route optimization algorithm of underwater vehicle automatic navigation and provide a reference for energy consumption calculations and route safety evaluations.

In the realm of non-destructive testing and health monitoring of plate-shell structures, there is a growing emphasis on ultrasonic Lamb wave testing. Especially, it is imperative to investigate methods for achieving damage detection using a limited number of sensors under modal interference. This study proposes a damage imaging approach that leverages multi-path scattering Lamb waves within a multi-modal framework (i.e., the existing of both A0 mode and S0 mode). The proposed method integrates coded excitation and pulse compression techniques to introduce a damage feature index termed AWLSCC (adaptive weighted local-signal correlation coefficient), which exhibits greater sensitivity compared to the conventional signal amplitude utilized in traditional approaches. Through the alignment of edge-reflected signals with their respective wave packets, this method amplifies the strength of faint reflected wave packets while mitigates potential interferences like modal overlap. Consequently, this technique significantly enhances imaging resolution and accuracy in the identification of cracks in various orientations.

In nonlinear ultrasonics (NLU), in addition to the well-known second harmonic generation (SHG), a static component (SC) generation occurs when a primary ultrasonic wave propagates through a solid with quadratic elastic nonlinearity. The generation of SC does not need to satisfy the phase velocity matching condition requested for SHG. Despite SC’s inherent advantages, previous reviews on NLU have frequently overlooked this nonlinear phenomenon. This paper presents a comprehensive review on the SC generation when ultrasonic waves propagate in solid media. The theoretical analysis of SC generation from primary longitudinal waves and primary Lamb waves in an isotropic plate is elaborated in detail without considering the attenuation effects. The propagation characteristics of SC from primary longitudinal waves, Lamb waves in plates, and ultrasonic guided waves (UGWs) in pipe-like structures are elucidated. The experimental reception and measurement techniques of SC are discussed. The application of SC in evaluating microscale damage in various fields is presented. Conclusions and future perspectives are summarized at the end of this review.

Ultrasonic guided waves (UGWs) have demonstrated utility in rock bolt testing, yet the influence of ribs on UGW propagation characteristics within these structures remains largely unexplored. This investigation seeks to address this gap by conducting numerical and experimental analyses of UGW behaviour in rock bolts, with a focus on rib effects. Furthermore, a simplified energy ratio index is developed to enhance defect detection capabilities. The semi-analytical finite element (SAFE) method is employed to generate dispersion curves for rock bolts, revealing that rib presence increases UGW mode quantity and decreases the group velocity of the L(0,1) mode. Numerical and experimental studies of UGW propagation in rock bolts indicate that the amplitude of rib-reflected L(0,1) mode UGWs correlates positively with centre excitation frequency. The significance of these rib-reflected waveforms is underscored, as they may obscure damage-reflected UGWs, complicating defect identification. To address this challenge, a novel energy ratio index is proposed and validated. Results demonstrate that this index, when accounting for rib influence, effectively detects defects in rock bolts.

In this paper, a method for weld defect identification from ultrasonic signals using the Gramian Angular Summation Field (GASF) and an improved deep convolutional generative adversarial network and residual network (DCGAN-ResNet) is proposed to overcome the problems of small-sample imbalance of echo signals as well as the low identification accuracy and poor efficiency of traditional convolutional neural networks (CNN). Firstly, the DCGAN model is improved based on Wasserstein distance and spectral normalisation, and the augmented dataset is used to validate its effectiveness. Then, the residual block for the ResNet model is improved using group convolution to enhance the nonlinear representation of the network and reduce the number of parameters and computations. Finally, the squeeze-and-excitation (SE) attention mechanism is introduced for feature recalibration to enhance attention to important features and recognition efficiency. Experimental results show that the improved DCGAN-ResNet method outperforms other commonly used methods in terms of feature extraction, recognition accuracy and efficiency for weld defects, and its test accuracy reaches 91.99%, which is 14.36% higher than that before dataset augmentation. Thus, the proposed method is effective and feasible for weld defect recognition from ultrasonic signals under small-sample imbalance conditions, and can also be applied to other pattern recognition fields.

The miniaturisation, ultra-thinness and high-density multi-layer structure of advanced microelectronic packages complicate the propagation mechanism of ultrasonic waves. In this paper, a finite element model is used to simulate ultrasonic wave propagation in flip chip packages, investigating the laws of transmission and reflection at the lamination boundaries. The acoustic field of ultrasonic transducers is simulated using MATLAB and Abaqus software. The angular spectrum method (ASM) based on the Fourier transform is adopted to more precisely reveal the distribution characteristics and attenuation relationship of near‐field ultrasonic waves. The influence of the frequency and size of the ultrasonic transducer on the propagation characteristics of ultrasonic waves is analysed. Based on an acoustic field map generated by the detection model, the waveform conversions of acoustic waves in a multi-layer structure are analysed. The results show that ultrasonic waves are mainly presented in the form of reflected and transmitted waves at the layered interface and the model with a perfectly matched layer (PML) has higher accuracy. Therefore, this method is applied to ultrasonic testing in a flip chip package, which cannot only effectively exclude interference from boundary reflection but also greatly improve the reliability of waveform conversions analysis.

The Total Focusing Method (TFM) focuses pixels using the Delay and Sum (DAS) beamforming technique, which relies solely on the temporal information of the full matrix capturing dataset while ignoring its spatial information, and the image resolution and contrast achievable with TFM are limited. In this work, a parallel sparse delay multiply and sum (PSDMAS) focusing imaging algorithm based on sparse arrays and parallel computing is proposed to improve contrast resolution and imaging efficiency. A sparse array optimisation method is applied to reduce the amount of data. A ratio of main-lobe width and side-lobe peak was constructed as the fitness function and a genetic algorithm was used to find the optimal solution for the array arrangement. Delay Multiply and Sum (DMAS) was employed to enhance the spatial coherence and suppress the clutter artefacts. Parallel computing strategies were implemented to improve imaging efficiency. To validate the effectiveness of the algorithm, we processed the full matrix data collected from simulations and experiments using PSDMAS, the imaging results of the PSDMAS provided a considerable improvement in Array Performance Indicator (API), and better lateral spatial resolution was also achieved. The computation time of PSDMAS was reduced by 99.9% compared to conventional DMAS.

Lamb wave inspection is a promising candidate for real-time monitoring of hidden corrosion in thin-walled metallic structures. The time-of-flight (ToF) which represents the variation of group velocity due to thickness reduction is commonly used, but its sensitivity is limited at the early stage of corrosion. To address this issue, the phase shift as a function of the phase velocity is defined and evaluated for corrosion detection in this paper. Specifically, the analytic cross-correlation method is applied, which estimates the group delay and the phase shift between two Lamb wave responses before and after the existence of corrosion simultaneously. The simulated examples show that the phase shift performs an obvious improvement on corrosion sensitivity compared to the ToF, and its value increases nearly linearly as the corrosion gets deeper. Ultimately, an experimental example is also introduced, where an active sensor network is deployed and a probabilistic imaging algorithm is introduced. The imaging results demonstrate that the proposed method could successfully identify and accurately localize the hidden corrosion.

Titanium alloy plate is widely used in automotive, aerospace, medical and other fields. Ultrasonic testing is a highly efficient non-destructive testing method for the inspection of titanium alloy components. However, there are strong distortion and attenuation of sound wave propagation in titanium alloys due to the presence of anisotropy and inhomogeneities, making it difficult to detect tiny defects in such components. In addition, some configuration parameters, such as the water path depth also affect the distribution of the acoustic field in the detected components. It is necessary to predict the acoustic field distribution to achieve a better testing accuracy. An acoustic field simulation method based on a multi-Gaussian beam is proposed, which can model the focused acoustic field in a multilayer anisotropy medium. The relationship between the acoustic focused area and the water path depth was explored and optimised comprehensively. C-scan imaging of specimen with flat bottom holes was performed at different water path depths. The results show that the proposed method can optimise the configuration parameters to improve the accuracy of the flaw sizing. This study provides an effective method for testing titanium alloy plate.

The miniaturization, ultra-thin and multi-layer complex structure of microelectronic packaging complicates the coupling acoustic field of ultrasonic waves and internal defects in the packaging, making accurate defect detection very difficult. In this paper, the finite element models of flip chip (FC) packaging and ball grid array (BGA) packaging are established to investigate the coupling acoustic field characteristics of ultrasonic waves and defects. In addition, based on the ultrasonic pitch and catch technique, the coupling laws of ultrasonic waves of different frequencies and the defects of different types, positions and sizes are analyzed by simulation, and the relationship between the relative amplitudes of the bottom waves and the sizes of different defects is revealed. Two specimens of microelectronic packaging are designed and fabricated to carry out the experimental studies using an ultrasonic signal acquisition system. The simulation and experimental results show that the relationship between the defects with small changes in the same location and the relative amplitudes of the bottom waves is basically linear, while the relationship between the solder ball extension defects with large changes and the relative amplitudes of the bottom waves is basically logarithmic, which provides a theoretical guidance for accurate evaluation of the type, size and location of defects in the practical detection.

In digital holography, the speckle noise caused by the coherent nature of the light source and the light scattering generated by the light path system degrade the quality of the reconstructed image seriously. Therefore, in this paper, we propose what we believe to be is a novel noise reduction method combining bidimensional empirical mode decomposition (BEMD) with the variational method, termed BEMDV. The reconstructed image is first decomposed into a series of bidimensional intrinsic mode function (BIMF) components with different frequencies using the BEMD method, and then a certain number of BIMF components are selected for noise reduction by the variational method. An improved particle swarm optimization algorithm is adopted to optimize the key parameters of the proposed method, so as to further improve its noise reduction performance. A reflective off-axis digital holographic imaging system is used to collect the holograms of the coin and optical resolution plate, and the experimental research on noise reduction is carried out. The results with qualitative and quantitative analyses show that the proposed method achieves a better performance on noise reduction and detail preservation than other general methods, enormously enhancing the image quality of holographic reconstruction.

In digital holography (DH), the key parameters in the optical path structure, such as diffraction distance, optical path difference and deflection angle, affect greatly the image quality of holographic reconstruction. Thus, it is crucial to determine the appropriate values of parameters. In this paper, the influence of these parameters on the quality of reconstructed images is deeply analysed through the DH principle and numerical simulation. A method to optimize automatically the parameters in the optical path is proposed based on an improved particle swarm optimization algorithm. An experimental setup for reflective off-axis DH is constructed, and coin and optical resolution plate are taken as test samples to perform the experimental research. Experimental results demonstrate that the proposed method can not only effectively enhance the quality of reconstructed images in the light of five indicators but also reduce the complexity and difficulty of adjusting the optical path parameters in the experiments.