基于模态试验的连接结构状态表征参数研究

有效辨识螺栓连接结构的连接状态,是确保结构可靠性和安全性的基础.传统的基于模态参数辨识结构连接状态变化的方法效果不太理想,从模态理论出发,在基准模态空间中构造了相对模态复/实连接刚度两类特征参数表征结构的连接状态,并采用不同拧紧力矩下的螺栓连接梁模态试验对构造的表征参数的有效性进行了检验.结果表明,所构造的表征参数比固有频率对连接状态变化具有更好的敏感性.     

基于声发射原理的螺栓连接状态辨识方法研究

振动环境中螺栓连接结构会发生碰摩现象,产生声发射信号。利用声发射参数分析方法对声发射检测信号进行时域分析,结果表明：振铃计数率随振动台加速度的提高而提高;螺栓连接结构在碰摩作用下产生出的能量与施加给螺栓的预紧扭矩的取值呈现一定的规律性。基于上述结果可以利用声发射进行螺栓连接结构的状态辨识。     

螺栓法兰连接结构简化动力学建模及模型参数辨识

螺栓法兰连接结构内含接触、间隙等多种非线性因素,导致该结构的动力学分析变得异常复杂且耗时,构建简化动力学模型是提高计算效率的主要手段之一。把螺栓法兰连接处理为黑盒子,并截取螺栓法兰连接结构的部分竖直部段,以形成"局部连接结构"。获得螺栓法兰连接对接面的接触特性后,采用若干个拉压刚度不同的弹簧阻尼单元对单个螺栓连接进行了简化,进而得到了局部连接结构的简化动力学模型。根据典型动载荷作用下的动力学响应数据,采用力状态映射法对简化动力学模型进行了参数辨识。动力学试验数据表明,螺栓法兰连接结构简化动力学模型的预测误差不超过20%。     

振动环境中螺栓连接结构状态声发射试验研究

螺栓连接应用广泛,螺栓松动可能造成灾难性的后果。结合声发射技术,在振动台上进行螺栓连接状态检测初步试验。对试验中所采集的大量声发射信号进行分析,找出不同扭矩下螺栓连接状态所对应的信号特征,实现对连接螺栓松动状态的辨识。     

SPCC钢滑动摩擦与拉伸声发射信号比较分析

为了将声发射技术应用在金属塑性加工中的摩擦监测中,以SPCC钢在拉伸过程与相对运动速度为100mm/min、正压力为7.5kg的摩擦过程产生的声发射信号特征为研究对象,分别用数据统计、平均值等方法对比分析了两种声发射信号的能量、振铃计数、幅度等参数。实验结果表明:SPCC钢拉伸的声发射能量值要远小于摩擦声发射能量值;拉伸的声发射振铃计数分布范围要略大于摩擦声发射振铃计数分布范围;拉伸的声发射幅度要小于摩擦声发射的幅度。最后对出现该现象的原因进行了探讨。     

混凝土静态轴拉声发射试验相关参数研究

摘要：对声发射采集系统的硬件参数设置、滤噪参数设置以及声发射信号特征参数及其相关性进行了系统的试验研究。共进行了11组断铅人工激发源试验和13个混凝土试件的单轴静态拉伸试验,采用全数字化的参数-波形式声发射采集系统和三种不同型号的传感器同步采集并存储了试验过程中的声发射特征参数和波形,应用参数和波形分析相结合的方法以及波形事后提取分析等技术手段对数据进行分析。结果表明：前置放大器增益、阈值、波形采样率和采样长度、带通滤波器等有一合理参数取值;幅度、振铃、持续时间、声发射信号能量、绝对能量、信号强度这6个参数能够较好地体现混凝土轴拉损伤过程的阶段性特征;声发射幅度、振铃数、持续时间、上升时间和信号强度5个参数之间存在显著的相关性。研究成果可为采用声发射技术研究混凝土的损伤破坏过程提供参考依据。     

应力波作用下岩石声发射实验研究

在霍普金森（SHPB）冲击系统上进行了应力波作用下岩石声发射实验,获得了该加载条件下岩石破裂的声发射规律。实验结果表明,应力波下,声发射峰值能量之前所出现的声发射信号相当匮乏;而且其能量规律呈现出两种明显不同的特征：I型,声发射峰值能量之后,能量迅速衰减,到了加载的末期,能量出现了一定的回升,产生了“拐点”;II型,声发射峰值能量之后,能量衰减相对I型较慢,且不出现“拐点”。研究结果表明,声发射的能量特征与岩石破碎的程度密切相关;冲击载荷作用下岩石的声发射不仅仅是裂纹信息的反映,其中也包含了对应力波信号的反映。     

局域波法在海洋平台结构声发射信号监测中的应用

摘要：将局域波用于分析导管架海洋平台模型的声发射信号,以获得声发射信号的时频特征和频率能量分布。通过局域波分解将声发射信号分解为一组基本模式分量（IMF）,对每一个IMF分量进行希尔伯特变换获得信号能量随时间频率的变化;由局域波时频谱得到边际谱,反映声发射信号的能量频率分布特征。通过分析导管架海洋平台模型模拟声发射信号,获得了特征,运用局域波分析方法监测到导管架海洋平台预制裂纹声发射信号的出现。因此,局域波法可以用于分析导管架海洋平台的声发射信号,在声发射信号处理领域将会有广阔的应用前景。     

螺栓连接非线性振动特性研究

摘要：在不同的外界激励下,螺栓连接常常表现出一定的非线性。为研究螺栓连接的非线性特性,设计了一个螺栓连接系统,并对其进行不同基础激励下的正弦扫频实验。通过实验数据的分析得到了螺栓连接的共振频率和相对阻尼系数,发现其随着激励量级的不同呈现出较明显的非线性特性。在理论模型研究中,将系统简化为非线性弹簧、非线性阻尼器、质量块的单自由度模型,并利用实验数据对非线性方程中的系数进行了识别。利用此非线性模型,求出系统的主共振频率和相对阻尼系数,它们与实验结果相比差别较小,说明文中所给的非线性方程能较好描述螺栓连接的振动特性。     

基于小波包分析的拉索损伤声发射信号特征提取

结合显式有限元和小波包分析技术开展了拉索损伤声发射信号特征提取的仿真分析。采用ANSYS/LS-DYNA模拟得到拉索损伤声发射信号的仿真信号,基于小波包能量谱对拉索声发射的有限元仿真信号进行了特征提取,从小波包分解层次、特征频带数量的选择及特征参数的噪声鲁棒性三个方面开展了讨论分析。结果表明:(1)通过选择适当的小波包分解层次,小波包能量谱可以精细地反映信号的特征;(2)选取少数特征频带就能使得小波包能量谱反映声发射信号的特征信息;(3)基于小波包能量谱的特征参数具有良好的损伤敏感性及噪声鲁棒性,能在强噪声影响下实现对拉索不同损伤类型的判别。     

基于EMD的胶合板损伤声发射信号特征提取及神经网络模式识别

摘要：针对胶合板损伤声发射信号的非平稳性和损伤类别特征相互重叠的实际情况,提出了基于经验模态分解(Empirical Mode Decomposition, EMD)和BP神经网络相结合的信号特征提取和识别方法。首先对损伤声发射信号进行EMD分解,筛选出包含主要信息的本征模态函数（Intrinsic Mode Function, IMF）分量;其次构建以各IMF分量的能量占比作为表征各损伤信号的特征向量;最后以提取的特征向量为输入样本,建立BP神经网络模式分类器对四类胶合板损伤信号进行识别。五层胶合板损伤的实测数据表明,该方法能够准确地提取出声发射信号特征并对其损伤类型进行有效地识别。     

Detection of fibre fracture and ply fragmentation in thin-ply UD carbon/glass hybrid laminates using acoustic emission

This paper investigates the link between acoustic emission (AE) events and the corresponding damage modes in thin-ply UD carbon/glass hybrid laminates under tensile loading. A novel configuration was investigated which has not previously been studied by AE, where the laminates were fabricated by embedding thin carbon plies between standard thickness translucent glass plies to produce progressive fragmentation of the carbon layer and delamination of the carbon/glass interface. A criterion based on amplitude and energy of the AE event values was established to identify the fragmentation failure mode. Since the glass layer was translucent, it was possible to quantitatively correlate the observed fragmentation during the tests and the AE events with high amplitude and energy values. This new method can be used as a simple and advanced tool to identify fibre fracture as well as estimate the number and sequence of damage events that are not visible e.g. in hybrid laminates with thick or non-transparent layers as well as when the damage is too small to be visually detected.     

Feasibility and limitations of damage identification in composite materials using acoustic emission

One of the current challenges in health monitoring of composite materials is the use of acoustic emission to identify damage modes. Many classification procedures have been reported in the literature but none of them clearly state limitations to their applicability, making it difficult to quantify them in different testing conditions. In the present paper, a method is described to characterize energy attenuation and how it affects AE signals features based solely on AE signals recorded during mechanical tests. Limitations to damage identification based on AE signals features can therefore be defined. The method is demonstrated on AE signals recorded during tensile tests on four different layups of carbon fiber reinforced polymer composites using signals frequency centroids to describe AE sources.     

Rapid evaluation of fatigue limit in AZ31B magnesium alloy using acoustic emission

Acoustic emission (AE) was used in a fatigue experiment to characterise AE signals and to rapidly determine the fatigue limit of AZ31B magnesium (Mg) alloy. The AE signals during fatigue were characterised according to waveforms and frequency. Meanwhile, the energy dissipation in the process of fatigue, which was represented by the accumulative AE energy, can be used to determine the fatigue limit. Based on the AE parameters, the fatigue limit was 97?MPa, with an 8% error value when compared with the results obtained by the conventional S–N curve method. This model only requires the accumulated energy of the signals during strain hardening. Therefore, the fatigue limit can be determined rapidly.     

Optimal design and testing of laminated light-weight composite structures with local reinforcements considering strength constraints: Part II: Testing

The present paper explains details of the measurement methods applied in the testing of strength-optimized locally reinforced laminates whose design has been explained in Part I. The measurement methods are an optical surface method, based on Digital Image Correlation (DIC), and measurements of Acoustic Emission (AE) which can originate from anywhere within the test specimen. Both methods are intended to identify early damage events or accumulation and the corresponding loads are compared to predicted first-ply-failure loads. The DIC method identifies non-linearities in the displacement path which infer that damage events have occurred. The AE method utilizes the signal energy rate per time to identify damage events. The results of the particular specimens show no correlation between the two measurement methods. The averaged values for the different type specimens show a significant dependency.     

Effect of martensite phase volume fraction on acoustic emission signals using wavelet packet analysis during tensile loading of dual phase steels

This paper will investigate the application of wavelet-based Acoustic Emission (AE) signal processing on micromechanisms to identify failure in dual phase steels (DPS)s. The AE signals from a tensile test using a range of DPS with different volume fractions of martensite (VM)s, in the range of 11–65% VM, were obtained and their waveforms were decomposed into various wavelet levels, each of which was related to a specific frequency range. Each level includes precise details, or approximations, of the so-called components. The energy percentage of each component was obtained by comparing it with the total energy of the AE signal. The energy distribution criterion in each component indicates that the energy in the AE signals is essentially concentrated on two or three components within a distinct frequency range. Each frequency range is related to a separate micromechanism, identifying failure. The results found for low VM in the contribution of ferrite/martensite interface decohesion figure prominently because 48% of their total energy was related to this micromechanism for a sample with 11% VM. The contribution of martensite phase fracture increased from 12% to 48.3% of total energy with an increase of VM in the range of 11% to 65% VM. The results were verified with microscopic observations and they indicate that wavelet-based signal processing is an efficient tool in the analysis of AE signals to detect micromechanisms identifying failure in DPS.     

基于VMD-PNN的砂轮钝化声发射检测

在磨削加工过程中,加工刀具即砂轮会发生钝化现象,砂轮表面磨损影响加工精度和工件质量,需要及时检测并修整.磨粒的塑性变形、破碎、断裂等会产生声发射信号,能够作为精确识别砂轮钝化状态的依据,且不易被噪声干扰,因此提出一种基于变分模态分解(Variational Mode Decomposition,VMD)和概率神经网络(...     

基于小波变换与统计分析的转子碰摩声发射特性研究

为解决转子碰摩损伤声发射信号分类及解释难题,应用PCI-2声发射系统和WS-ZHT1型多功能转子实验台组成转子碰摩声发射检测系统,采集转子局部碰摩声发射信号,通过理论分析声发射信号特征和小波基函数性质,dB8小波适合提取声发射信号特征;碰摩产生大量声发射信号,大量声发射信号的统计特性蕴涵较多碰摩信息。对不同转速条件下不同检测位置碰摩声发射信号的统计分析表明,声发射信号的功率谱密度集中在100～400 kHz。声发射信号平均幅值、平均能量可作为区分转子碰摩程度特征参数;功率谱主频可作为区分声发射相对位置特征参数,结论与碰摩类型无关。     

小波分析和相平面在声发射信号处理中的应用

为了精确地获取声发射源信息,必须采取有效手段对声发射信号进行分析。小波分析同时具有时-频局部化的优良特性,用小波自适应地对声发射信号进行多分辨率的分解,在相平面中绘制系数能量图可以同时直观地观察声发射信号的频率、时间、能量等信息。同时,相平面系数能量图也为小波重构声发射信号提供了理论依据,在此基础上重构的声发射信号排除了其它信号的干扰,更能表征声发射源信息,对声发射源的分析有重要的意义。     

单轴多级循环加载岩石声发射分形特性试验研究

通过对三种岩石试样进行单轴循环加载试验,获得岩石试样加载过程中声发射事件率、能量率和空间位置分布数据。运用相空间重构理论直接从时间序列上通过G-P算法求得事件率和能量率关联分维,根据柱覆盖法求解得到声发射事件源空间分布关联分维。研究结果表明,声发射事件率、能量率和空间分布都具有分形特性,且声发射源空间分布分形特性最为显著。相空间的选取对关联维数有一定影响,相空间取值为4时能够较好地计算关联维数。将不同加卸载循环声发射源空间分布的变化趋势和声发射源空间分布关联维数变化趋势进行对比发现,声发射关联维数能够很好地反映岩石内部损伤破坏的发展。在等压加卸载条件下,声发射源空间分布关联维数显著突增。随着加卸载循环应力的增加,关联维数总体呈下降趋势,在达到较高应力时,关联维数则在较小区间内波动或下降趋势变得极其缓慢。该特点可以作为矿山地压微震监测预警的参考依据。