管道声发射信号衰减特性随管道壁厚变化研究

为了建立压力管道泄漏声发射衰减定位模型,需要开展压力管道泄漏声发射试验研究。由于试验管道无法按1∶1比例采用核电站压力管道原型,有必要针对相同材质和加工工艺、不同壁厚和外径的金属管道,获取声发射信号在不同壁厚的金属管道中的传播特性规律及差异,揭示金属管道壁厚对声发射波传播衰减的影响。分别采用3种不同尺寸规格的金属管道作为试验对象,运用声发射探测技术获得声发射信号沿着不同壁厚的金属管壁传播的衰减规律,揭示了金属管道壁厚对声发射波传播的影响。结果表明,管道声发射信号的衰减特性随着管道壁厚的不同而变化,在薄壁管中声发射波在传播路径中会产生模式转换,即发生频散现象,各种模式的波相互叠加使信号幅度沿着传播路径未呈单调衰减趋势,用声发射信号的衰减特性做定位时须考虑这一点。     

声发射检测技术在轴承球检测中的应用

相同条件下运动的轴承球,下落到装有换能器的反弹块上,通过换能器使接受的信号转换成电信号,经过放大、滤波等形成可记录的AE信号。根据RI／t,可将缺陷球识别出来,实现无损检测。此方法具有速度快、可靠性高、易于实现自动化等优点。     

声发射技术在金属材料检测中的应用研究

声发射技术作为一种常见的物理检测手段,本身具有整体性、动态性的特征,将其应用与金属材料检查,能实时性地实现金属材料无损检测,这对于材料早期故障和破坏预报具有积极作用。本文就金属材料检测声发射技术的应用要点展开分析。     

声发射技术在起重机无损检测中的现状

对起重机械常规无损检测技术(目视检测、磁粉检测、超声检测、应力测试、振动测试等)和声发射技术进行了比较,归纳了声发射技术在起重机械无损检测中的研究现状,提出了研究的基本思路,讨论了起重机械声发射技术中的几个关键问题,即典型声发射源信号的获取、声发射信号处理技术、声发射源的模式识别。最后,对起重机械声发射检测技术的研究前景进行了展望。     

声发射检测技术及其在核电厂的应用前景

介绍声发射检测技术在核电厂反应堆压力容器检验中的应用情况,并展望了其在核电厂其他方面应用的广阔前景。     

基于声发射信号的砂轮钝化在线检测方法

提出了一种基于声发射信号的砂轮钝化在线检测方法,该方法利用BP神经网络建立磨削声发射(AE)信号幅值变化特征量与砂轮钝化状况之间的非线性关系模型,并利用实验数据对该模型进行训练测试,训练获得的模型可用于在线检测小批量、多品种磨削条件下砂轮的钝化状况.实验结果表明,该方法能够准确地在线检测砂轮的钝化程度,具有很高的实用价值.     

声发射技术在复合材料发展中的应用

介绍了声发射技术的基本原理、重要特征和检测技术以及在复合材料研究与发展领域中的应用。着重阐述了国内外声发射技术在复合材料力学性能和结构完整性检测研究方面的应用现状，进而展望了声发射技术在未来复合材料无损评价与质量控制领域的应用前景。     

刀具磨损的声发射特性

刀具磨损的声发射特性哈尔滨理工大学赵彦玲董丽华严复钢在金属切削过程中，刀具磨损的可靠性和促进自动化有着重要作用。人们曾利用电视摄像机、切削力、加工表面粗糙度变化等方法进行监视和检测，但是这些方法在实际应用中都不够可靠，本文介绍一种既方便又可靠的用于刀...     

基于声发射定位算法的故障检测技术研究

高精度的声发射源定位能够帮助工程人员快速定位故障或异常发生的位置,可用于对大型设备进行结构可靠性监测和检测。为了提高声发射源定位的精度,提出一种融合了Geiger定位方法、同伦算法及牛顿梯度方法的源定位算法。采用Geiger迭代定位方法实现对源定位信息的迭代更新,并将更新信息的求解转变为对不适定问题的求解。同伦算法和牛顿梯度方法的结合,避免了常规不适定问题求解方法中耗时的矩阵求逆,优化了求解过程。实验结果表明,所提方法优于常规定位算法,可实现对声发射源位置信息的高精度求解。     

基于声发射信号的钢桶泄漏特性试验研究

基于企业验漏需求,设计搭建了钢桶泄漏试验台和钢桶泄漏声发射信号检测系统。通过实验研究了不同泄漏工况下,钢桶泄漏产生的声发射信号特性。试验表明钢桶泄漏声发射信号频域特征出现在90~100k Hz。通过小波包分解,提取了90~100k Hz频段的能量作为泄漏特征。得到了钢桶的泄漏能量特征和信号均方根值(RMS)随泄漏孔径尺寸、钢桶内部加压压力和声发射传感器位置的变化规律。建立了基于钢桶声发射信号的泄漏检测规则,为实际应用过程中选择泄漏特征参数、加压压力和选择传感器位置及个数提供了参考。     

基于声发射信号监测的高速准干切削试验研究

声发射信号可以及时地反映出高速切削加工过程中工件和刀具材料的内部缺陷变化和扩展情况.为了对其进行监测和分析,建立了一套基于虚拟仪器的信号采集系统,采集高速切削铝合金加工过程中的声发射信号.对采集到的声发射信号进行小波变换并重构后,发现铝合金切削过程中的声发射信号与切削速度密切相关.随着切削速度的增大,声发射也随着增大.     

On-line data acquisition system for acoustic emission signals

An electronic system for processing and recording some very important parameters of acoustic emission signals at a relatively high speed is described. The system is applicable to almost any process in which acoustic emission is implemented for quality control. Data processing is done in real time. The processed data are ring-down count (threshold crossing), energy, duration, and time of occurrence of each acoustic emission burst.     

基于中值滤波-SVD和EMD的声发射信号特征提取

针对随机噪声和脉冲干扰对经验模态分解(EMD)质量的影响,提出中值滤波和奇异值分解(SVD)联合降噪方法,并将其与EMD分解相结合形成一种新的声发射(AE)信号特征提取方法.首先对原始AE信号进行中值滤波,去除幅值较大的异常值;其次对去除异常值的信号序列进行相空间重构和SVD分解,并针对难以确定重构阶数这一问题,提出奇异值能量差分谱概念,利用谱峰的较大值位置来确定重构阶数,以进一步降噪;最后对降噪信号进行EMD分解,以本征模态函数(IMF)的能量占比作为表征各损伤信号的特征向量.数值仿真和5层胶合板损伤的实测数据表明,该方法不仅能够滤除噪声干扰,提高EMD分解的时效性和准确性,而且能够有效地提取出胶合板AE信号特征,对其损伤类型进行有效地识别.     

Analysis of experimentally generated friction related acoustic emission signals

Acoustic emission signals generated by sliding friction between two flat steel surfaces are characterized. A test fixture to simulate the reciprocating motion between the two surfaces under controlled conditions is developed. Sliding friction under several combinations of surface roughness, relative velocity, and normal pressure was examined. Wideband AE sensors and instrumentation were used for acquiring and analyzing the acoustic emission signals. Acoustic emission events occurred primarily during the slip portion of the stick-slip cycles. AE waveform features obtained during these experiments were indicative of the tribological conditions. Frequency components in excess of 700 kHz were seen during these experiments. The characteristics of the experimentally observed acoustic emission signals were in general agreement with earlier numerical predictions. Friction related acoustic emission signals were distinguishable from those from other sources such as fatigue crack growth. The characterization of friction related acoustic emission signals is likely to be of value in many tribological and structural health monitoring applications.     

Monitoring of through-feed centreless grinding processes with acoustic emission signals

Grinding is a mechanical removal process applied mainly in finishing operations of hardened workpieces to produce small tolerances with high-quality. Especially, centreless grinding is broadly used in serial production due to the requirement of high accuracy in process. Centreless grinding is used to produce several mechanical components such as, bushings, needles, ball bearings, valves, and stems for shock absorbers. However, the setup of machine tools is very complex and needs long time due to the great number of input variables that should be checked and configured. The acoustic emission monitoring can be used to help the first setup or during the grinding process becoming a on-line detection system. Considering the importance of obtaining an efficient methodology to predict and detect the surface quality and the dimensional errors, a monitoring of the frequency on the spectrum of acoustic emission (EA) was conducted, related to surface roughness Rz, cylindricity, and roundness. The FFT and Wavelet were applied aiming to help the analysis of data and provide the best understanding of the signal and generating an intelligent information in the automation in grinding process. Thus, in this work the results showed that the analysis of the harmonic content of acoustic emission signal is a powerful tool to monitoring the centreless grinding process.     

Characterization of nugget nucleation quality based on the structure-borne acoustic emission signals detected during resistance spot welding process

Acoustic emission signals detected during the resistance spot welding of aluminum alloy were studied in order to assess the characterizations of welding process, the characterizations of the effect of welding parameters to nugget nucleation and the characterizations of the nugget quality by the analysis to acoustic emission signals. The results showed that the physical phases of nugget nucleation can be characterized by the acoustic emission signals detected during the resistance spot welding process. The effects of welding current and current duration to nugget nucleation can be characterized by the characteristic parameters of acoustic emission signals. The characteristic parameters of acoustic emission signals had a better relevance to nugget dimensions and weld strength, which made it possible to measure or predict the weld strength by the characteristic parameters of acoustic emission signals detected during the resistance spot welding process.     

Evaluation of grinding wheel loading phenomena by using acoustic emission signals

In the industrial manufacturing field, machining is a major process. Machining operations involve grinding, drilling, milling, turning, pressing, molding, and so on. Among these operations, grinding is the most precise and complicated process. The surface condition of the grinding wheel plays an important role in grinding performance, and the identification of grinding wheel loading phenomena during the grinding process is critical. Accordingly, this present study describes a measurement method based on the acoustic emission (AE) technique to characterize the loading phenomena of a Si₂O₃ grinding wheel for the grinding mass production process. The proposed measurement method combines the process-integrated measurement of AE signals, offline digital image processing, and surface roughness measurement of the ground workpieces for the evaluation of grinding wheel loading phenomena. The experimental results show that the proposed measurement method provides a quantitative index from the AE signals to evaluate the grinding wheel loading phenomena online for the grinding mass production process, and this quantitative index is determined via some experiments in advance in the same grinding environment to help the monitoring and controlling of the grinding process.     

Investigation on the effect of EDM process variables and environments on acoustic emission signals

Abstract

The performance of electrical discharge machining (EDM) primarily depends on the spark quality generated in the inter-electrode gap (IEG) between the tool and workpiece. A method for obtaining accurate information about the spark gap is required to effectively monitor the EDM process. The rise and fall of thermal energy in the discharge zone at a rapid rate during the dielectric breakdown produces high-pressure shock waves. This work explores the suitability of using acoustic emission (AE) generated from these shock waves and the elastic AE waves released on the workpiece due to the induced stress to monitor the performance and spark gap in EDM. The information content of the AE signals acquired at various machining conditions was extracted using AE RMS, spectral energy and peak amplitude. These features were able to well discriminate the machining condition, tool material, workpiece material, flushing pressure, current density, the initial surface roughness of the tool. Additionally, the AE signal features had a good and consistent correlation with the performance parameters, including material removal rate, surface roughness (Ra and Rq) and tool wear. The findings lay the groundwork to develop an effective, non-intrusive in-situ AE-monitoring system for performance and IEG condition in EDM.     

风力机叶片多裂纹扩展声发射信号的特征识别

针对风力机叶片蒙皮多裂纹难以状态识别的问题,根据裂纹扩展释放能量的过程,推导主裂纹扩展AE信号的表达式,从而明晰了主裂纹扩展的AE信号特性及其与应力变化之间的关联。由于多裂纹扩展AE信号为卷积混合模型,提出一种对具有非平稳、非线性特性的卷积混合AE信号特征提取的方法,以输出信号的广义能量作为目标函数得到盲解卷的滤波器迭代式,采用Godard算法通过输出信号与估计值的误差调整滤波器系数,并根据相似系数选择适当的非线性函数以减少采集设备对AE信号的影响。最后在裂纹扩展试验中,预制不同尺寸的多缺陷,对叶片试件同时施加激振载荷和循环载荷,每间隔一定的循环次数采集不同状态的AE信号,同时采用具有非全局性的瞬时频率和特征尺度来识别多裂纹在不同扩展状态下的特征,从而明晰了信号特征与多裂纹生存状态的关联,形成了识别多裂纹复合材料损伤的评价机制。