Search Results (65)

The wind turbine blade is subject to multi-source impacts, such as bird strikes, lightning strikes, and hail, throughout its extended service. Accurate localization of those impact sources is a key technical link in structural health monitoring of the wind turbine blade. In this paper, a single-sensor impact source localization method is proposed. Capitalizing on deep learning frameworks, this method innovatively transforms the impact source localization problem into a classification task, thereby eliminating the need for anisotropy compensation and correction required by conventional localization algorithms. Furthermore, it leverages the inherent coding effects of the blade’s material and geometric anisotropy on impact sources originating from different positions, enabling localization using only a single sensor. Experimental results show that the method has a high localization accuracy of 96.9% under single-sensor conditions, which significantly reduces the cost compared to the traditional multi-sensor array scheme. This study provides a cost-effective solution for real-time detection of wind turbine blade impact events. Full article

It is vital to stabilize pillar dams in underground reservoirs in coal mine goafs to protect groundwater resources and quarry safety, practice green mining, and protect the ecological environment. Considering the actual occurrence of coal pillar dams in underground reservoirs, acoustic emission (AE) mechanical tests were performed on dry, naturally absorbed, and soaked coal samples. According to the mechanical analysis, Quantitative analysis revealed that dry samples exhibited the highest mechanical parameters (peak strength: 12.3 ± 0.8 MPa; elastic modulus: 1.45 ± 0.12 GPa), followed by natural absorption (peak strength: 9.7 ± 0.6 MPa; elastic modulus: 1.02 ± 0.09 GPa), and soaked absorption showed the lowest values (peak strength: 7.2 ± 0.5 MPa; elastic modulus: 0.78 ± 0.07 GPa). The rate of mechanical deterioration increased by ~25% per 1% increase in moisture content. It was identified that the internal crack development presented a macrofracture surface initiating at the sample center and expanding radially outward, and gradually expanding to the edges by adopting AE seismic source localization and the K-means clustering algorithm. Soaked absorption was easier to produce shear cracks than natural absorption, and a higher water content increased the likelihood. The b-value of the AE damage evaluation index based on crack development was negatively correlated with the rock damage state, and the S-value was positively correlated, and both effectively characterized it. The research results can offer reference and guidance for the support design, monitoring, and warning of coal pillar dams in underground reservoirs. (The samples were tested under two moisture conditions: (1) ‘Soaked absorption’—samples fully saturated by immersion in water for 24 h, and (2) ‘Natural absorption’—samples equilibrated at 50% relative humidity and 25 °C for 7 days). Full article

This study presents an advanced NO_x reduction strategy for a 330 MW lignite-fired boiler using an immunological AI system: the SILO (Stochastic Immune Layer Optimizer) combustion optimizer inspired by artificial immune systems. The immunological AI optimizer adaptively models multi-variable interactions and fireball shape in real time, optimizing fuel–air mixing to reduce NO_x formation at the source. Unlike reactive secondary methods, the combustion optimizer reshapes the combustion process to reduce emissions while improving efficiency. Real-time temperature data from the AGAM acoustic system inform the combustion optimizer’s fireball modeling, ensuring combustion uniformity. A urea-based SNCR system serves as a secondary layer, controlled based on local furnace conditions to target thermal zones. Field results confirmed that SILO reduced NO_x emissions below 200 mg/Nm³, decreased urea consumption by up to 34%, and improved boiler efficiency by 0.29%. The architecture offers a scalable, DCS-integrated solution for aligning fossil-fueled operations with tightening emission standards. Full article

A theoretical model is presented for the accurate detection of a gas leak source through a pinhole in a cylindrical storage vessel using the acoustic emission (AE) technique. Pinholes of various diameters ranging from 0.20 to 1.2 mm were installed as leak sources, and safe N₂ was used as a filler gas. AE signals were measured and analyzed in terms of AE parameters (such as frequency, amplitude and RMS) as a function of angle and axial distance. Among them, the amplitude characteristic was the most important parameter to determine the leakage dynamics of AE with a continuous waveform. The simulation of AE amplitude was performed using the theoretical model for AE. For practical applications, the theoretical formula was modified into two semi-empirical equations by introducing the normalization method to fit the angular and axial characteristics of the observed AE amplitude, respectively. The main finding of this study is that the semi-empirical equations provide an accurate solution for leak source localization in the cylindrical vessel. As a priori knowledge, the value of ${\kappa }_{\eta }$ in Green’s function, which determines the angular and axial dependence of the AE amplitude, was determined by applying external excitation to the cylinder surface. The proposed formulas provide a suitable approach for practical application in the localization of leak sources in cylindrical storage tanks. Full article

Partial discharges (PDs) are one of the leading causes of catastrophic power transformer failures. To prevent such failures, online PD monitoring systems are increasingly being implemented. In this paper, to the best of the authors’ knowledge, a case study analysis of short-term PD monitoring is presented for the first time using a combination of acoustic emission and ultra-high-frequency methods. Studies have shown that this approach, supported by selected statistical methods for analyzing the convergence (such as the confusion matrix and agreement metrics) of acoustic and electromagnetic pulse detection, improves the reliability of PD detection. Furthermore, it was shown that short-term PD monitoring enables the identification of time windows during which discharges occur periodically and the determination of the transformer phase containing the PD source. This, in turn, facilitates the application of the time difference of arrival (TDoA) technique for the precise localization of transformer insulation defects. Full article

Acoustic sensing technology has attracted significant attention across various fields, including mechanical fault early warning and wireless communication, due to its high information density and advantages in remote wireless applications. However, environmental noise reduces the signal-to-noise ratio (SNR) in traditional acoustic systems. In response, this article proposes a novel Z-shaped channel gradient metamaterial (ZCGM) that leverages strong wave compression effects coupled with effective medium theory to detect weak signals in complex environments. The properties of the designed metamaterials were verified by theoretical derivation and finite element simulation of the model. Compared to conventional linear gradient acoustic metamaterials (GAMs), ZCGM demonstrates significantly superior performance in acoustic enhancement, with a lower capture frequency. Furthermore, the structure exhibits flexible tunability in its profile. In addition, the center frequency of each actual air gap is determined in this paper based on the swept frequency signal test. Based on this center frequency, a preset specific harmonic acoustic signal is used as an emission source to simulate the actual application scenario, and experiments are constructed and conducted to verify the performance of the designed metamaterials. The results consistently show that ZCGM has distinct advantages and promising application prospects in the detection, enhancement, and localization of weak acoustic signals. Full article

The paper considers the results of experiments on localization of the sources of geoacoustic radiation generated in near-surface sedimentary rocks. Geoacoustic signals from sources were recorded by a spaced sensor system consisting of two combined receivers and a hydrophone. The system was installed near the bottom of a natural water body (Mikizha lake) in Kamchatka. Radiation sources were located by two methods, a triangulation survey and estimation of the signal arrival time difference from spaced receivers. Coordinates for more than 40 sources were measured, and their space distribution was mapped. As the result of the experiment, it was stated that geoacoustic radiation sources are located in bottom rocks at the depths up to 2.20 ± 0.25 m at the distances of up to 8 ± 0.25 m. Localization of geoacoustic radiation sources is topical for projecting a new alarm system for the notification on the possibility of strong earthquake occurrence. The results of the analysis of the frequency of rock AE source generation and accurate estimation of their location will be the basis of this system. Full article

The acoustic analysis of a moving object, such as in pass-by or fly-over tests, is a very important and demanding issue. These types of analyses make it possible to characterize the machine in quite realistic conditions, but the typical difficulties related to source localization and characterization are usually exacerbated by the need to take into consideration and to compensate for the object movement. In this paper, a technique based on acoustic beamforming is proposed, which is applicable to all those cases where the object under investigation is moving. In the proposed technique, the object’s movement is not regarded as a problem but as a resource, enabling a virtual increase in the number of microphone acquisitions. For a stationary acoustic emission from a moving object, each time segment of the acquired signal is treated as if it is coming from a microphone (virtual) positioned differently relative to the object’s reference system. This paper describes the technique and presents examples of results obtained from both simulated and real signals. Performance analysis is conducted and discussed in detail. Full article

This paper develops benchmark cases for low- and very-low-frequency passive acoustic source localization (ASL) using synthetic data. These cases can be potentially applied to the detection of turbulence-generated low-frequency acoustic emissions in the atmosphere. A deep learning approach is used as an alternative to conventional beamforming, which performs poorly under these conditions. The cases, which include two- and three-dimensional ASL, use a shallow and inexpensive convolutional neural network (CNN) with an appropriate input feature to optimize the source localization. CNNs are trained on a limited dataset to highlight the computational tractability and viability of the low-frequency ASL approach. Despite the modest training sets and computational expense, detection accuracies of at least 80% and far superior performance compared with beamforming are achieved—a result that can be improved with more data, training, and deeper networks. These benchmark cases offer well-defined and repeatable representative problems for comparison and further development of deep learning-based low-frequency ASL. Full article

In this paper, we propose a novel approach to coordinate-based acoustic emission (AE) source localization to address the challenges of limited and imbalanced datasets from fiber-optic AE sensors used for structural health monitoring (SHM). We have developed a hybrid deep learning model combining four generative adversarial network (GAN) variants for data augmentation with an adapted inception neural network for regression-based prediction. The experimental setup features a single fiber-optic AE sensor based on a tightly coiled fiber-optic Fabry-Perot interferometer formed by two identical fiber Bragg gratings. AE signals were generated using the Hsu-Nielsen pencil lead break test on a grid-marked thin aluminum plate with 35 distinct locations, simulating real-world structural monitoring conditions in bounded isotropic plate-like structures. It is demonstrated that the single-sensor configuration can achieve precise localization, avoiding the need for a multiple sensor array. The GAN-based signal augmentation expanded the dataset from 900 to 4500 samples, with the Wasserstein distance between the original and synthetic datasets decreasing by 83% after 2000 training epochs, demonstrating the high fidelity of the synthetic data. Among the GAN variants, the standard GAN architecture proved the most effective, outperforming other variants in this specific application. The hybrid model exhibits superior performance compared to non-augmented deep learning approaches, with the median error distribution comparisons revealing a significant 50% reduction in prediction errors, accompanied by substantially improved consistency across various AE source locations. Overall, this developed hybrid approach offers a promising solution for enhancing AE-based SHM in complex infrastructures, improving damage detection accuracy and reliability for more efficient predictive maintenance strategies. Full article

Acoustic emission (AE) source localization technology, since the early application to one-dimensional structures, has been extended to a wide range of applications to two-dimensional (2D) structures, including isotropic and anisotropic materials, which are currently the most widely studied and the most mature. With the development of AE source localization technology, more and more significant challenges have arisen for three-dimensional (3D) structures, which are mostly anisotropic and have complex propagation paths. This paper summarizes and discusses the AE source localization methods in different dimensions as well as their applications, including the main methods for 2D AE source localization, such as the triangulation method, beam forming, strain rosette technique, modal AE, artificial neural network, optimization and the time reversal technique, as well as state-of-the-art AE source localization methods in isotropic and anisotropic structures utilizing these methods. Recent advances in AE source localization in complex 3D structures are also reviewed. Full article

Acoustic emissions (AEs) are produced by elastic waves generated by damage in solid materials. AE sensors have been widely used in several fields as a promising tool to analyze damage mechanisms such as cracking, dislocation movement, etc. However, accurately determining the location of damage in solids in a non-destructive manner is still challenging. In this paper, we propose a crack wave arrival time determination algorithm that can identify crack waves with low SNRs (signal-to-noise ratios) generated in rocks. The basic idea is that the variances in the crack wave and noise have different characteristics, depending on the size of the moving window. The results can be used to accurately determine the crack source location. The source location is determined by observing where the variance in the crack wave velocities of the true and imaginary crack location reach a minimum. By performing a pencil lead break test using rock samples, it was confirmed that the proposed method could successfully find wave arrival time and crack localization. The proposed algorithm for source localization can be used for evaluating and monitoring damage in tunnels or other underground facilities in real time. Full article

To detect damage in mechanical structures, acoustic emission (AE) inspection is considered as a powerful tool. Generally, the classical acoustic emission detection method uses a sparse sensor array to identify damage and its location. It often depends on a pre-defined wave velocity and it is difficult to yield a high localization accuracy for complicated structures using this method. In this paper, the passive guided wave phased array method, a dense sensor array method, is studied, aiming to obtain better AE localization accuracy in aluminum thin plates. Specifically, the proposed method uses a cross-shaped phased array enhanced with four additional far-end sensors for AE source localization. The proposed two-step method first calculates the real-time velocity and the polar angle of the AE source using the phased array algorithm, and then solves the location of the AE source with the additional far-end sensor. Both numerical and physical experiments on an aluminum flat panel are carried out to validate the proposed method. It is found that using the cross-shaped guided wave phased array method with enhanced far-end sensors can localize the coordinates of the AE source accurately without knowing the wave velocity in advance. The proposed method is also extended to a stiffened thin-walled structure with high localization accuracy, which validates its AE source localization ability for complicated structures. Finally, the influences of cross-shaped phased array element number and the time window length on the proposed method are discussed in detail. Full article

Acoustic Emission (AE) testing is a non-destructive evaluation technique that has gained significant attention in pipeline monitoring. Pencil-lead breaks (PLBs) are commonly used in reproducing and characterising sensors used in AE applications and have emerged as a valuable tool for calibration processes. This technique involves breaking a pencil lead by pressing it on the surface of the test structure and applying a bending moment at a given angle on a surface. The applied force produces a local deformation on the test surface, which is released when the lead breaks. The fracture in these PLBs is assumed to be a step unload; however, this is not the case. In this work, a series of PLB source experiments complemented with parallel numerical simulations were carried out to investigate the actual unload rate by correlating the relationship between AE speed, frequency, and power from PLBs. This was achieved by varying the simulation unload rates recorded over a duration of 2 s on a steel pipe and comparing to the experiment. Analysis of the investigated results from the experimental and numerical models suggests that although the AE line structure of a PLB can be reproduced by simulation for short times only (1 µs), the actual unload rate for PLBs is in the region of 10–8 s. It is concluded that FEA has the potential to help in the recovery of the temporal structure from real AE structures. The establishment of this model will provide a theoretical basis for future studies on the monitoring of non-impulsive AE sources such as impact on pipelines using finite element analysis. Full article

The time difference of arrival (TDOA) method has traditionally proven effective for locating acoustic emission (AE) sources and detecting structural defects. Nevertheless, its applicability is constrained when applied to anisotropic materials, particularly in the context of fiber-reinforced composite structures. In response, this paper introduces a novel COmposite LOcalization using Response Surface (COLORS) algorithm based on a two-step approach for precise AE source localization suitable for laminated composite structures. Leveraging a response surface developed from critical parameters, including AE velocity profiles, attenuation rates, distances, and orientations, the proposed method offers precise AE source predictions. The incorporation of updated velocity data into the algorithm yields superior localization accuracy compared to the conventional TDOA approach relying on the theoretical AE propagation velocity. The mean absolute error (MAE) for COLORS and TDOA were found to be 6.97 mm and 8.69 mm, respectively. Similarly, the root mean square error (RMSE) for COLORS and TODA methods were found to be 9.24 mm and 12.06 mm, respectively, indicating better performance of the COLORS algorithm in the context of source location accuracy. The finding underscores the significance of AE signal attenuation in minimizing AE wave velocity discrepancies and enhancing AE localization precision. The outcome of this investigation represents a substantial advancement in AE localization within laminated composite structures, holding potential implications for improved damage detection and structural health monitoring of composite structures. Full article