Acoustic Emission

声发射
  • 文章类型: Journal Article
    三段滑坡以其巨大的规模而闻名,隐蔽的开发过程,和毁灭性的影响。这项研究进行了物理模型试验,以模拟一种称为三段内滑坡的特殊地质结构。使用视频图像对测试样品的失效过程和前兆特征进行了细致的分析,微震(MS)信号,和声发射(AE)信号,专注于事件活动,强度,和频率。提出了一种新的基于AE波形特征的分类方法,将AE信号分为突发信号和连续信号。这些发现揭示了这些信号演变的明显差异。突发信号仅在裂纹扩展和失效阶段出现。在这些阶段,突发信号的累积AE命中逐渐增加,振幅上升然后下降。高振幅突发信号主要分布在中高频带。相比之下,连续信号的累积AE命中迅速升级,随着振幅单调增加,高振幅连续信号主要分布在低频段。突发信号和高频AE信号的出现表明微裂纹的产生,作为预警指标。值得注意的是,AE信号的预警点比视频图像和MS信号的预警点更早。此外,突发信号的预警点比连续信号的预警点发生得更早,分类方法的预警点先于总体AE信号。
    Three-section landslides are renowned for their immense size, concealed development process, and devastating impact. This study conducted physical model tests to simulate one special geological structure called a three-section-within landslide. The failure process and precursory characteristics of the tested samples were meticulously analyzed using video imagery, micro-seismic (MS) signals, and acoustic emission (AE) signals, with a focus on event activity, intensity, and frequency. A novel classification method based on AE waveform characteristics was proposed, categorizing AE signals into burst signals and continuous signals. The findings reveal distinct differences in the evolution of these signals. Burst signals appeared exclusively during the crack propagation and failure stages. During these stages, the cumulative AE hits of burst signals increased gradually, with amplitude rising and then declining. High-amplitude burst signals were predominantly distributed in the middle- and high-frequency bands. In contrast, cumulative AE hits of continuous signals escalated rapidly, with amplitude monotonously increasing, and high-amplitude continuous signals were primarily distributed in the low-frequency band. The emergence of burst signals and high-frequency AE signals indicated the generation of microcracks, serving as early-warning indicators. Notably, the early-warning points of AE signals were detected earlier than those of video imagery and MS signals. Furthermore, the early-warning point of burst signals occurred earlier than those of continuous signals, and the early-warning point of the classification method preceded that of overall AE signals.
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  • 文章类型: Journal Article
    目的研究水饱和煤样经微波循环后的损伤程度和渗流特征。利用微波设备对煤样进行微波循环处理。利用非接触数字图像处理技术和声发射系统对煤样进行三轴加载实验研究,能量演化模式,不同微波周期时间下煤样的声发射信息和渗透率特征。研究结果表明:随着微波循环次数的增加,煤样表面逐渐出现密集的网格加载裂缝,煤样的三轴局部应力下降,菌株也减少了,弹性模量和泊松比也降低;在致密化阶段,耗散能量高于弹性能量,随着弹性阶段的进行,弹性能量逐渐反转以超过耗散能量,煤样的总能量和弹性能随着循环次数的增加而减少,和耗散的能量上升;煤样由于微波周期数的增加而产生大量的裂隙,加载过程中裂缝活动越频繁,随着循环次数的增加,声发射振幅和振铃计数散射点都变得密集,数据增加;初始渗透率,破坏性渗透率和平均渗透率都增加了,微波处理有较好的增渗效果,处理后的煤样渗透率从低渗透率变为中渗透率,渗透率增强在6个周期中最大,渗透率提高了7.18倍。本文探讨了微波循环处理水饱和煤样的破坏条件。然后,探讨了微波循环对煤体增渗的影响,为探索井下低渗透煤样的气体增渗和提取提供了一种新的方法。
    To investigate the extent of damage and seepage characteristics of water-saturated coal samples after subjecting them to microwave cycling. The microwave equipment was used to process the coal samples by microwave cycling. The non-contact digital image processing technology and acoustic emission system were used to carry out the triaxial loading experimental study of the coal samples to obtain the mechanical parameter characteristics, energy evolution pattern, acoustic emission information and permeability characteristics of coal samples under different microwave cycle times. The results of the study show that: With the increase in the number of microwave cycles, dense grid-loaded cracks gradually appeared on the surface of the coal samples, the triaxial partial stresses of the coal samples decreased, and the strains also decreased, and the modulus of elasticity and Poisson\'s ratio also decreased; In the densification stage stage, the dissipated energy is higher than the elastic energy, and as the elastic stage proceeds, the elastic energy gradually reverses to exceed the dissipated energy, and the total energy and elastic energy of the coal samples decrease with the increase in the number of cycles, and the dissipated energy rises; Coal samples produce a large number of fissures due to the increase in the number of microwave cycles, the more frequent the fissure activity during the loading process, the acoustic emission amplitude and ringing count scattering points all become dense with the increase in the number of cycles, and the data increase; Initial permeability, destructive permeability and average permeability were all increased, microwave treatment has a better effect of permeability enhancement, the permeability of the treated coal samples was changed from low permeability to medium permeability, and the permeability enhancement was the largest in 6 cycles, and the permeability was increased by 7.18 times. This article explores the damage condition of water-saturated coal samples under microwave cycling treatment. Then, it explores the effect of microwave cycling on the permeability enhancement of the coal body, which provides a new method for exploring the gas permeability enhancement and extraction of low-permeability coal samples underground.
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  • 文章类型: Journal Article
    声发射(AE)技术已被广泛用于监测SiC晶片的研磨过程。AE信号的时域特征值的均方根(RMS)与材料去除率(MRR)具有线性关系。然而,背景噪声的存在严重降低了信号监测精度。噪声干扰通常导致RMS偏差增加和信号失真。在这份手稿中提出的研究中,结合小波包降噪和谱减法降噪技术,提出了一种频率阈值降噪方法。在固定研磨垫上进行了三组SiC研磨实验,并使用三种不同的降噪方法处理研磨声信号:频率阈值,小波包,和谱减法。结果表明,采用频率阈值的降噪方法是最有效的,具有RMS与MRR的线性拟合的最佳确定系数(R2)。
    Acoustic emission (AE) technology has been widely utilized to monitor the SiC wafer lapping process. The root-mean-square (RMS) of the time-domain eigenvalues of the AE signal has a linear relationship with the material removal rate (MRR). However, the existence of background noise severely reduces signal monitoring accuracy. Noise interference often leads to increased RMS deviation and signal distortion. In the study presented in this manuscript, a frequency threshold noise reduction approach was developed by combining and improving wavelet packet noise reduction and spectral subtraction noise reduction techniques. Three groups of SiC lapping experiments were conducted on a fixed abrasive pad, and the lapping acoustic signals were processed using three different noise reduction approaches: frequency threshold, wavelet packet, and spectral subtraction. The results show that the noise reduction method using the frequency threshold is the most effective, with the best coefficient of determination (R2) for the linear fit of the RMS to the MRR.
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  • 文章类型: Journal Article
    在操作条件下监测晶体材料内部的过程对光电子学和科学仪器非常感兴趣。早期缺陷检测确保了多个基于晶体的器件的正常运行。在这项研究中,提出了声发射(AE)传感和交叉偏振成像的组合,用于快速表征晶体的结构。对于实验,选择二氧化碲(TeO2)晶体是因为其在声光中的广泛使用。在单轴压缩载荷下进行研究,同时采集AE信号和四个偏振光学图像。对AE数据的时间依赖性和光去偏振特征的二维图进行了分析,以建立不可逆损伤引发和裂纹状缺陷形成的定量标准。获得的结果揭示了这些过程特有的偏振图像模式和AE脉冲持续时间变化,它们为在工作条件下实时无损监测光学透明晶体的结构开辟了新的可能性。
    Monitoring the processes inside crystalline materials under their operating conditions is of great interest in optoelectronics and scientific instrumentation. Early defect detection ensures the proper functioning of multiple crystal-based devices. In this study, a combination of acoustic emission (AE) sensing and cross-polarization imaging is proposed for the fast characterization of the crystal\'s structure. For the experiments, tellurium dioxide (TeO2) crystal was chosen due to its wide use in acousto-optics. Studies were performed under uniaxial compression loading with a simultaneous acquisition of AE signals and four polarized optical images. An analysis of the temporal dependencies of the AE data and two-dimensional maps of the light depolarization features was carried out in order to establish quantitative criteria for irreversible damage initiation and crack-like defect formation. The obtained results reveal the polarization image patterns and the AE pulse duration alteration specific to these processes, and they open up new possibilities for non-destructively monitoring in real-time the structure of optically transparent crystals under their operating conditions.
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  • 文章类型: Journal Article
    揭示复杂复合结构的力学性能和损伤机理,包括回填和围岩,对于确保向下进路回填采矿方法的安全发展至关重要。这项工作在各种加载条件下对回填岩石进行双轴压缩试验。使用DIC和声发射(AE)技术分析损伤过程,同时探讨了不同加载阶段AE事件的分布。此外,通过多重分形分析研究了试样的主要破坏形式。阐明了回填-岩石组合的损伤演化规律。结果表明,DIC和AE提供了一致的样品损伤描述,在不同加载条件下,回填-岩石复合试件的损伤演化差异显著,为工程现场安全保护提供有价值的见解。
    Unveiling the mechanical properties and damage mechanism of the complex composite structure, comprising backfill and surrounding rock, is crucial for ensuring the safe development of the downward-approach backfill mining method. This work conducts biaxial compression tests on backfill-rock under various loading conditions. The damage process is analyzed using DIC and acoustic emission (AE) techniques, while the distribution of AE events at different loading stages is explored. Additionally, the dominant failure forms of specimens are studied through multifractal analysis. The damage evolution law of backfill-rock combinations is elucidated. The results indicate that DIC and AE provide consistent descriptions of specimen damage, and the damage evolution of backfill-rock composite specimens varies notably under different loading conditions, offering valuable insights for engineering site safety protection.
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  • 文章类型: Journal Article
    本研究提出了一种基于极限梯度增强模型的冲击后剩余抗压强度预测方法,重点研究复合材料层合板作为所研究的材料体系。在受控的温度和湿度条件下进行声发射测试,以收集特征参数,在小样本条件下,建立这些参数与残余抗压强度之间的映射关系。该模型准确地预测了冲击后层压板的残余抗压强度,测试集的决定系数和均方根误差分别为0.9910和2.9174。人工神经网络模型和极限梯度提升模型的性能比较表明,在数据量小的情况下,与人工神经网络相比,极限梯度增强模型具有更高的准确性和鲁棒性。此外,利用SHAP方法分析了声发射特征参数的灵敏度,揭示了峰值振幅等指标,环计数,能源,峰值频率显著影响剩余抗压强度的预测结果。本文提出的基于机器学习的复合材料层合板损伤容限评估方法,利用声发射技术的全局监测优势,快速预测复合材料层合板冲击后的残余抗压强度,为复合材料层合板结构健康在线监测提供了理论方法。该方法适用于不同冲击条件下的各种复合材料层压板结构,证明了其广泛的适用性和可靠性。
    This study proposes a prediction method for residual compressive strength after impact based on the extreme gradient boosting model, focusing on composite laminates as the studied material system. Acoustic emission tests were conducted under controlled temperature and humidity conditions to collect characteristic parameters, establishing a mapping relationship between these parameters and residual compressive strength under small sample conditions. The model accurately predicted the residual compressive strength of the laminates after impact, with the coefficient of determination and root mean square error for the test set being 0.9910 and 2.9174, respectively. A comparison of the performance of the artificial neural network model and the extreme gradient boosting model shows that, in the case of small data volumes, the extreme gradient boosting model exhibits superior accuracy and robustness compared to the artificial neural network. Furthermore, the sensitivity of acoustic emission characteristic parameters is analyzed using the SHAP method, revealing that indicators such as peak amplitude, ring count, energy, and peak frequency significantly impact the prediction results of residual compressive strength. The machine-learning-based method for assessing the damage tolerance of composite laminates proposed in this paper utilizes the global monitoring advantages of acoustic emission technology to rapidly predict the residual compressive strength after the impact of composite laminates, providing a theoretical approach for online structural health monitoring of composite laminates. This method is applicable to various composite laminate structures under different impact conditions, demonstrating its broad applicability and reliability.
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  • 文章类型: Journal Article
    声发射的频域特性可以反映在时域参数中难以分析的岩石结构和应力条件等问题。研究浸泡时间对泥质矿物岩石力学性质及声发射频域特征的影响,对于全面分析水岩耦合条件下的岩石变化具有重要意义。在这项研究中,在干燥条件下对含蒙脱石的砂岩进行了单轴压缩试验和声发射试验,饱和,和不同的浸泡时间条件,重点分析了浸没时间对岩石声发射主频的影响。结果表明,浸泡时间对抗压强度有不同程度的影响,主要声发射频率的分布特征,主频的频率范围,砂岩失稳破坏的前兆信息。初始饱和后,岩石样品的强度从干燥状态下的53.52MPa下降到49.51MPa,浸泡30天后稳定下来.干燥和最初饱和的岩石样品均显示出三个主频带。经过不同的浸泡日,主频带出现在95kHz和110kHz之间。浸泡5天后,0kHz附近的主频带逐渐消失。浸泡60天后,35kHz至40kHz之间的主频带逐渐消失,随着浸泡时间的增加,声发射信号的主频增加。在干岩石样品的加载过程中,声发射信号的主频主要集中在0kHz到310kHz之间,饱和后,主频率均低于180kHz。干岩样破裂前最显著的特征是高频频繁发生和主频突变。在破裂之前,初始饱和和浸入样品5、10和30天的前兆事件的特征是主频率突然变化的出现和快速增加,以及主频率的频率范围的扩大。浸泡60天后,岩样破裂的前兆特征逐渐消失,主频率的突然变化经常发生在样品加载的不同阶段,这使得很难根据这些突然的变化准确预测标本的破裂。
    The frequency domain characteristics of acoustic emission can reflect issues such as rock structure and stress conditions that are difficult to analyze in time domain parameters. Studying the influence of immersion time on the mechanical properties and acoustic emission frequency domain characteristics of muddy mineral rocks is of great significance for comprehensively analyzing rock changes under water-rock coupling conditions. In this study, uniaxial compression tests and acoustic emission tests were conducted on sandstones containing montmorillonite under dry, saturated, and different immersion time conditions, with a focus on analyzing the effect of immersion time on the dominant frequency of rock acoustic emission. The results indicated that immersion time had varying degrees of influence on compressive strength, the distribution characteristics of dominant acoustic emission frequencies, the frequency range of dominant frequencies, and precursor information of instability failure for sandstones. After initial saturation, the strength of the rock sample decreased from 53.52 MPa in the dry state to 49.51 MPa, and it stabilized after 30 days of immersion. Both dry and initially saturated rock samples exhibited three dominant frequency bands. After different immersion days, a dominant frequency band appeared between 95 kHz and 110 kHz. After 5 days of immersion, the dominant frequency band near 0 kHz gradually disappeared. After 60 days of immersion, the dominant frequency band between 35 kHz and 40 kHz gradually disappeared, and with increasing immersion time, the dominant frequency of the acoustic emission signals increased. During the loading process of dry rock samples, the dominant frequency of acoustic emission signals was mainly concentrated between 0 kHz and 310 kHz, while after saturation, the dominant frequencies were all below 180 kHz. The most significant feature before the rupture of dry rock samples was the frequent occurrence of high frequencies and sudden changes in dominant frequencies. Before rupture, the characteristics of precursor events for initially saturated and immersed samples for 5, 10, and 30 days were the appearance and rapid increase in sudden changes in dominant frequencies, as well as an enlargement of the frequency range of dominant frequencies. After 60 days of immersion, the precursor characteristics of rock sample rupture gradually disappeared, and sudden changes in dominant frequencies frequently occurred at various stages of sample loading, making it difficult to accurately predict the rupture of specimens based on these sudden changes.
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  • 文章类型: Journal Article
    采空区干充填材料的压实特性和承载力对采场控制和地表稳定性有重要影响。通过声发射监测和力学模型分析,对不同粒径和塔尔博特系数的破碎废物进行了一系列的密闭压缩试验。变形,碎片化,并确定了相应工况下的声发射特性。结果表明,不同粒径和塔尔博特系数的碎石在有限压缩过程中的应力-应变曲线表现出相似的非线性行为。然而,应变响应随应力水平的变化而变化。通过分析应力-应变曲线的斜率变化率,废石的横向单轴压缩过程可分为三个变形阶段:快速压缩,稳定的破碎,和缓慢的压实。砾石的压缩变形特性因粒径和塔尔博特系数而异。具有较高的塔尔博特系数的试样在初始压实加载过程中表现出较强的抗压缩性和较弱的抗变形性。值得注意的是,内压结构强度受最大粒径D、分级系数n,和粒度分布的连续性,而不仅仅是大颗粒的比例。废石约束轴向压缩过程中声发射信号和能量-时间曲线的演化与压实过程同步。总的来说,在干碎废物充填中,压实对维持采空区的稳定性起着至关重要的作用。
    The compaction characteristics and bearing capacity of dry filling materials in goaf have a significant influence on stope control and surface stability. Through acoustic emission monitoring and mechanical model analysis, a series of confined compression tests were conducted on crushed waste with varying particle sizes and Talbot coefficients. The deformation, fragmentation, and acoustic emission characteristics under corresponding working conditions were determined. The results indicate that the stress-strain curves of crushed stone with different particle sizes and Talbot coefficients exhibit similar nonlinear behavior during confined compression. However, the strain response varies with changing stress levels. By analyzing the slope change rate of the stress-strain curve, the lateral uniaxial compression process of waste rock can be divided into three deformation stages: rapid compression, stable crushing, and slow compaction. The compressive deformation characteristics of gravel differ based on particle size and Talbot coefficient. Specimens with a higher Talbot coefficient demonstrate stronger compressive resistance and weaker deformation resistance during initial compaction loading. Notably, the internal pressure structure strength is influenced by factors such as maximum particle size D, grading coefficient n, and particle size distribution continuity, rather than solely by the proportion of large particles. The evolution of acoustic emission signals and energy-time curve during waste rock confined axial compression synchronizes with the compaction process. Overall, compaction plays a critical role in maintaining the stability of goaf in dry crushed waste filling.
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  • 文章类型: Journal Article
    传统上,到达时间差(TDOA)方法已被证明可有效地定位声发射(AE)源和检测结构缺陷。然而,当应用于各向异性材料时,其适用性受到限制,特别是在纤维增强复合材料结构的背景下。作为回应,本文介绍了一种基于两步法的响应面(COLORS)算法的新型复合材料局部化方法,用于精确的AE源定位,适用于层状复合材料结构。利用从关键参数开发的响应面,包括AE速度剖面,衰减率,距离,和方向,所提出的方法提供了精确的声发射源预测。与依赖于理论AE传播速度的常规TDOA方法相比,将更新的速度数据结合到算法中可产生更高的定位精度。颜色和TDOA的平均绝对误差(MAE)分别为6.97mm和8.69mm,分别。同样,颜色和TODA方法的均方根误差(RMSE)分别为9.24mm和12.06mm,分别,表明COLORS算法在源定位精度方面具有更好的性能。这一发现强调了AE信号衰减在最小化AE波速度差异和提高AE定位精度方面的重要性。这项研究的结果代表了层状复合结构中AE定位的实质性进步,对改善复合材料结构的损伤检测和结构健康监测具有潜在意义。
    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.
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  • 文章类型: Journal Article
    由纤维增强塑料制成的夹层结构由于其高强度重量比,通常用于海上船舶。耐腐蚀性,和浮力。了解水分吸收后的机械性能以及水分吸收对其结构完整性和安全性的影响在平面外加载状态下对于材料优化至关重要。使用声发射(AE)和机器学习(ML)等现代方法可以为评估机械性能和结构健康监测提供有效的技术。在这项研究中,采用了由E玻璃纤维面板与聚氯乙烯泡沫芯制成的夹层结构的准静态压痕测试获得的AE特征。然后使用时域和频域特征来捕获AE数据内的相关信息和模式。利用k-means++算法进行聚类分析,提供对所研究结构的主要损伤模式的见解。采用三种集成学习算法来开发暴露于和未暴露于海水的样品的损伤预测模型,并加载了不同几何形状的压头。所开发的模型有效地识别了在不同载荷条件下各种压头几何形状的所有损伤模式,精度得分在86.4和95.9%之间。这说明了ML在预测海洋应用的复合结构中的损伤演化方面的巨大潜力。
    Sandwich structures made with fibre-reinforced plastics are commonly used in maritime vessels thanks to their high strength-to-weight ratios, corrosion resistance, and buoyancy. Understanding their mechanical performance after moisture uptake and the implications of moisture uptake for their structural integrity and safety within out-of-plane loading regimes is vital for material optimisation. The use of modern methods such as acoustic emission (AE) and machine learning (ML) could provide effective techniques for the assessment of mechanical behaviour and structural health monitoring. In this study, the AE features obtained from quasi-static indentation tests on sandwich structures made from E-glass fibre face sheets with polyvinyl chloride foam cores were employed. Time- and frequency-domain features were then used to capture the relevant information and patterns within the AE data. A k-means++ algorithm was utilized for clustering analysis, providing insights into the principal damage modes of the studied structures. Three ensemble learning algorithms were employed to develop a damage-prediction model for samples exposed and unexposed to seawater and were loaded with indenters of different geometries. The developed models effectively identified all damage modes for the various indenter geometries under different loading conditions with accuracy scores between 86.4 and 95.9%. This illustrates the significant potential of ML for the prediction of damage evolution in composite structures for marine applications.
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