Search Results (1,449)

Different coal failure impact types exhibit different damage statuses and failure modes, resulting in distinct signal characteristics of infrared radiation (IR) and acoustic emission (AE). This paper combines IR and AE monitoring methods to innovatively establish coal damage and failure precursor warning models and obtains the IR and AE precursor characteristics for different coal failure impact types. This research shows that there is a good correspondence between IR and AE timing and spatial distribution of different coal impact types. As the impact tendency increases, the intensity of IR and AE signals increases with coal failure, and the AE positioning points and IR high-temperature areas tend to concentrate. The coal body gradually changes from tensile failure to shear failure. The shear cracks in the failure stage of coal with no, weak, and strong impact are 39.9%, 50.9%, and 53.7%, respectively. The IR and AE instability precursor point of coal with no, weak, and strong impact occurred at 55.2%, 66.3%, and 93.4% of coal failure, respectively. After the IR and AE combined instability precursor point, the dissipated energy and combined damage variable increase rapidly, and the coal body will undergo instability and failure. The research results provide a theoretical basis for comprehensive monitoring of coal body failure and rock burst. Full article

This paper introduces a novel network, DDFE-Transformer (Data-Driven Feature Extraction-Transformer), for fault diagnosis using acoustic emission signals. The DDFE-Transformer network integrates two primary modules: the DDFE module, focusing on noise reduction and feature enhancement, and the Transformer module. The DDFE module employs two techniques: the Wavelet Kernel Network (WKN) for noise reduction and the Convolutional Block Attention Module (CBAM) for feature enhancement. The wavelet function in the WKN reduces noise, while the attention mechanism in the CBAM enhances features. The Transformer module then processes the feature vectors and sends the results to the softmax layer for classification. To validate the proposed method’s efficacy, experiments were conducted using acoustic emission datasets from NASA Ames Research Center and the University of California, Berkeley. The results were compared using the four key metrics obtained through confusion matrix analysis. Experimental results show that the proposed method performs excellently in fault diagnosis using acoustic emission signals, achieving a high average accuracy of 99.84% and outperforming several baseline models, such as CNN, CNN-LSTM, CNN-GRU, VGG19, and ZFNet. The best-performing model, VGG19, only achieved an accuracy of 88.61%. Additionally, the findings suggest that integrating noise reduction and feature enhancement in a single framework significantly improves the network’s classification accuracy and robustness when analyzing acoustic emission signals. Full article

Global warming and climate change are major concerns across multiple disciplines. Population growth, urbanization, and industrialization are significant contributing factors to such problems due to the escalating use of fossil fuels required to meet growing energy demands. The building sector uses the largest share of total global energy production and produces tons of greenhouse gas emissions. Emerging eco-friendly technologies, such as solar and wind energy harvesting, are being extensively explored; however, they are insufficient. Nature-inspired technologies could offer solutions to our problems. For instance, algae are microorganisms that use water, light, and CO₂ to produce energy and sustain life, and the exploitation of these characteristics in a built environment is termed algae building technology, which is a very efficient and green application suitable for a sustainable future. Algae-integrated façades show great versatility through biomass and energy production, wastewater treatment, shading, and thermal and acoustic insulation. In this paper, algae will be introduced as a robust tool toward a greener and more sustainable future. Algae building technology and its implementation will be demonstrated. Furthermore, steps for applying this sustainable strategy in Egypt will be discussed. 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

The features of rock shear-slip fracturing are closely related to the stability of rock mass engineering. Granite, white sandstone, red sandstone, and yellow sandstone specimens were selected in this study. The loading phase of “shear failure > slow slip > fast slip” was set up to explore the correlation between fracture type, acoustic emission (AE) features, and energy dissipation during the rock fracturing process. The results show that there is a strong correlation between fracture type, energy dissipation, and AE features. The energy dissipation ratio of tension-shear (T-S) composite, shear, and tensile types is 10:100:1. The fracture types in the shear failure phase are mainly tensile and TS composite types. The differential mechanism of energy dissipation of different rocks during the shear-slip process is revealed from the physical property perspectives of mineral composition, particle size, and diagenetic mode. These results provide a necessary research basis for energy dissipation research in rock failure and offer an important scientific foundation for analyzing the fracture propagation problem in the shear-slip process. They also provide a research basis for further understanding the acoustic emission characteristics and crack type evolution during rock shear and slip processes, which helps to better understand the shear failure mechanism of natural joints and provides a reference for the identification of precursors of shear disasters in geotechnical engineering. Full article

Acoustic emission (AE) technology, as a non-destructive testing methodology, is extensively utilized to monitor various materials’ structural integrity. However, AE signals captured during experimental processes are often tainted with assorted noise factors that degrade the signal clarity and integrity, complicating precise analytical evaluations of the experimental outcomes. In response to these challenges, this paper introduces an unsupervised deep learning-based denoising model tailored for AE signals. It juxtaposes its efficacy against established methods, such as wavelet packet denoising, Hilbert transform denoising, and complete ensemble empirical mode decomposition with adaptive noise denoising. The results demonstrate that the unsupervised skip autoencoder model exhibits substantial potential in noise reduction, marking a significant advancement in AE signal processing. Subsequently, the paper focuses on applying this advanced denoising technique to AE signals collected during the tensile testing of steel fiber-reinforced concrete (SFRC), the tensile testing of steel, and flexural experiments of reinforced concrete beam, and it meticulously discusses the variations in the waveform and the spectrogram of the original signal and the signal after noise reduction. The results show that the model can also remove the noise of AE signals. 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

As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert–Huang transform (HHT) was applied to each source signal to obtain a joint time–frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%. Full article

In the process of roadway excavation in thick and hard coal seams with a hard roof, the instantaneous release of a large amount of elastic energy accumulated in coal and rock mass causes disasters. Especially under the action of tectonic stress, dynamic disasters of roadway-surrounding rock are extremely strong. Therefore, this paper takes the 110,505 roadway of the Yushuling Coal Mine as the engineering background. Aiming at the serious deformation of roadway-surrounding rock and the problem of strong mine pressure, the deformation mechanism of roadway-surrounding rock is studied by means of theoretical analysis, indoor experimentation, numerical simulation and field testing, and the surrounding rock control technology is proposed. Firstly, the results show that the stress field type of the Yushuling Coal Mine is a ${\sigma }_{Hv}$ type, the azimuth angle of the maximum horizontal principal stress is concentrated in 110.30°~114.12°, the dip angle is −33.04°~−3.43°, and the maximum horizontal principal stress is 1.94~2.76 times of the minimum horizontal principal stress. Secondly, the brittleness index of No. 5 is 0.62; the failure energy release of the surrounding rock compressive energy floor rock sample is up to 150,000 mv * ms. The more the cumulative number of rock samples, the greater the strength, and the more severe the damage. Thirdly, with the increase in tectonic stress, the stress of roadway-surrounding rock is asymmetrically distributed, and the plastic zone develops along the tendency. The maximum range of the plastic zone expands from 4.18 m to 10.19 m. Lastly, according to the deformation characteristics of roadway-surrounding rock, left side > roof > right side > floor, the surrounding rock control technology of ‘asymmetric anchor net cable support + borehole pressure relief’ is proposed, which realizes the effective control of roadway-surrounding rock deformation. Full article

The wave frequency and energy of traditional piezoelectric emission sources used in acoustic logging are limited, which results in an inadequate detection resolution for measuring small-scale geological formations. Additionally, the propagation of these waves in formations is prone to loss and noise interference, restricting detection to only a few tens of meters around the well. This paper investigates an impulse sound source, a new emission source that can effectively enhance the frequency range and wave energy of traditional sources by generating excitation waves through high-voltage discharges in a fluid-penetrated electrode structure. Firstly, a high-voltage circuit experimental system for the impulse sound source was constructed, and the discharge and response characteristics were experimentally analyzed. Then, four types of needle series electrode structure models were developed to investigate and compare the effects of different electrode structures on the impulse sound source, with the needle-ring electrode demonstrating superior performance. Finally, the needle-ring electrode structure was optimized to develop a ball-tipped needle-ring electrode, which is more suitable for acoustic logging. The results show that the electrode structure directly influences the discharge characteristics of the impulse sound source. After comparison and optimization, the final ball-tipped needle-ring electrode exhibited a broader frequency range—from zero to several hundred thousand Hz—while maintaining a high acoustic amplitude. It has the capability to detect geological areas beyond 100 m and is effective for evaluating micro-fractures and small fracture blocks near wells that require high detection accuracy. This is of significant importance in oil, gas, new energy, and other drilling fields. Full article

The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line monitoring technology using specific sensor signals can detect abnormal grinding wheel wear in a timely manner. However, due to the non-linearity and complexity of the grinding wheel wear process, as well as the interference and noise of the sensor signal, the accuracy and reliability of on-line monitoring technology still need to be improved. In this paper, an intelligent monitoring system based on multi-sensor fusion is established, and this system can be used for precise grinding wheel wear monitoring. The proposed system focuses on titanium alloy, a typical difficult-to-process aerospace material, and addresses the issue of low on-line monitoring accuracy found in traditional single-sensor systems. Additionally, a multi-eigenvalue fusion algorithm based on an improved support vector machine (SVM) is proposed. In this study, the mean square value of the wavelet packet decomposition coefficient of the acoustic emission signal, the grinding force ratio of the force signal, and the effective value of the vibration signal were taken as inputs for the improved support vector machine, and the recognition strategy was adjusted using the entropy weight evaluation method. A high-precision grinding machine was used to carry out multiple sets of grinding wheel wear experiments. After being processed by the multi-sensor integrated precision grinding wheel wear intelligent monitoring system, the collected signals can accurately reflect the grinding wheel wear state, and the monitoring accuracy can reach more than 92%. Full article

This work presents the study of a reproducible acoustic emission method based on the launching of a metallic sphere and low-cost piezoelectric diaphragm. For this purpose, tests were first conducted on a carbon fiber-reinforced polymer structure, and then on an aluminum structure for comparative analysis. The pencil-lead break (PLB) tests were also conducted for comparisons with the proposed method. Different launching heights and elastic deformations of the structures were investigated. The results show higher repeatability for the sphere impact method, as the PLB is more affected by human inaccuracy, and it was also effective in damage detection. Full article

Laboratory acoustic emissions (AEs) serve as small-scale analogues to earthquakes, offering fundamental insights into seismic processes. To ensure accurate physical interpretations of AEs, rigorous calibration of the acoustic system is essential. In this paper, we present an empirical calibration technique that quantifies sensor response, instrumentation effects, and path characteristics into a single entity termed instrument apparatus response. Using a controlled seismic source with different steel balls, we retrieve the instrument apparatus response in the frequency domain under typical experimental conditions for various piezoelectric sensors (PZTs) arranged to simulate a three-component seismic station. Removing these responses from the raw AE spectra allows us to obtain calibrated AE source spectra, which are then effectively used to constrain the seismic AE source parameters. We apply this calibration method to acoustic emissions (AEs) generated during unstable stick-slip behavior of a quartz gouge in double direct shear experiments. The calibrated AEs range in magnitude from −7.1 to −6.4 and exhibit stress drops between 0.075 MPa and 4.29 MPa, consistent with earthquake scaling relation. This result highlights the strong similarities between AEs generated from frictional gouge experiments and natural earthquakes. Through this acoustic emission calibration, we gain physical insights into the seismic sources of laboratory AEs, enhancing our understanding of seismic rupture processes in fault gouge experiments. 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

This paper experimentally characterises the far-field noise emissions of a rotor operating in reverse non-axial inflow conditions. Specifically, experiments were undertaken at a range of rotor tilting angles and inflow velocities to investigate the effects of negative tilting on rotor acoustics and their correlation with aerodynamic performance. The results show that the forces and moments experienced by the rotor blades change significantly with increasing inflow velocity and increasing negative tilting angle. Correspondingly, distinct modifications to the far-field acoustic spectra are observed for the negatively tilted rotor when compared to the edgewise condition, with the broadband noise content notably increasing. Moreover, for a given tilting angle, the broadband noise component is accentuated with increasing inflow velocity, similar to when the negative tilting angle is increased. With reference to the flow-field surveys conducted in the literature and a preliminary in-house flow measurement, the increase in broadband content can possibly be attributed to the heightened level of ingestion of blade self-turbulence, i.e., the ingestion of turbulent wake generated by the upstream portion of the rotor by the downstream portion. At lower inflow velocities, the magnitude of the blade passing frequency at each of the observer angles is found to change minimally with negative tilt. In contrast, at higher inflow velocities, the directivity pattern and intensity of both the blade passing frequency and the overall sound pressure level are shown to change with increases in magnitude, particularly at downstream observer locations with negative tilt. These findings have important ramifications for the design and suitable operational profile of aerial vehicles for future urban air mobility applications. Full article