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.     

基于Sagnac光纤声发射传感器研究

基于光纤Sagnac干涉效应,建立一套光纤声发射传感系统,并分析其系统构成和测量原理,建立标准声发射采集系统并进行室内实验,完成对三个声源信号:断铅信号、摩擦信号和敲击信号的采集测试。实验结果表明,与传统声发射检测系统相比,新型光纤Sagnac声发射检测系统具有整体简单,操作简易,设备体积小等优点。     

声发射监控磨削过程中影响因素的实验研究

本文利用自制AE-I型声发射磨削检测仪进行试验，在分析磨削接触前后声发射信号变化的基础上，指出磨削接触前后声发射信号会出现很大变化，可以根据声发射信号的变化作为磨削接触的标志；在分析磨削过程中影响声发射信号大小的因素基础上，指出了影响声发射信号大小的主要因素，并提出了砂轮钝化的检测方法。     

数控车床加工过程中的刀具声发射试验研究

利用SWAES声发射检测仪提取数控加工过程中金属切削声发射信号,对信号进行频谱分析,找出主要频段,并利用金属切削过程中的声发射模型及试验数据,对影响声发射信号的因素进行分析,指出进给速度和主轴转速对声发射信号影响较大.     

重型车刀破损声发射监控装置

根据声发射信号特征,用声发射信号判断刀具破损,开发研制出“重型切削刀破损声发射监控装置。”此装置用８０９８芯片为微处理器进行振铃计数、声发射信号实时分析处理。当刀具破损或严重磨损时,监控装置发出声光报警信号,并使车床横向退刀。信号响应时间小于１ｓ,其报警成功率达９０％。     

AE技术在离心泵转子部件故障诊断中的应用研究

离心泵的故障主要集中在转子部件,其故障发生的过程中伴随有丰富的声发射信号,故通过分析其信号特征可得出故障的相应信息.采用特征参量、分布分析方法及波形的时域分析方法对声发射信号进行了分析,并确定了事件计数率、RMS电压和振铃计数率作为声发射故障诊断的特征参量.同时验证了针对早期磨损类故障声发射诊断方法比振动诊断方法更好的诊断效果.     

基于声发射的Q235钢点蚀过程监测技术研究

以声发射技术为基础,针对油气储运常用材料Q235的点蚀过程,利用试件在模拟溶液中点蚀过程产生的声发射信号为样本,采用参量分析与波形分析相结合,重点研究Q235钢点蚀过程中产生的声发射信号的变化特征,得到了金属点蚀不同阶段的声发射信号特性,并通过声发射信号事件的分布规律,得到了Q235钢早期腐蚀与声发射信号演变的一致性结果。试验证明了声发射技术为基础的Q235钢点蚀过程监测具有良好效果。     

基于光纤光栅和支持向量机的声发射定位系统

利用光纤光栅传感器和边缘滤波原理构建传感系统,结合小波分解与重构和支持向量机算法,对铝合金板声发射定位进行了研究。根据划分区域进行声发射实验,探索声发射源所在区域与信号特征之间的关系。在对声发射信号进行小波分解的基础上,使用近似系数和细节系数进行重构,并对重构后的各信号计算其振荡能量作为信号特征,进行声发射区域识别。以重构信号的振荡能量作为输入、声发射区域位置类别作为输出构建支持向量机多分类模型,实现了声发射区域定位识别。实验结果表明,在400mm×400mm×2mm的铝合金板上对36个测试样本进行了多次声发射区域定位识别,在180次模拟实验中实现了176次声发射区域准确定位,正确率达到97.78%,声发射区域识别精度为30mm×30mm。该研究结果为机械结构的声发射区域定位检测提供了有效方法。     

基于声发射和神经网络的风机叶片裂纹识别研究

提出一种对风力机叶片裂纹声发射信号进行模式识别的方法。该方法以叶片无裂纹、萌生裂纹、扩展裂纹和断裂四个阶段为声发射源的四个模式,基于声发射信号含有丰富的发射源信息的特点,通过大量采样获得叶片裂纹声发射信号参数,并依照叶片裂纹声发射参数分析的数值特点确定BP神经网络,用选定的网络对叶片裂纹阶段进行模式识别,以判断裂纹的危害程度。仿真结果表明,利用BP神经网络可以对声发射信号进行有效识别,识别准确率达到90%以上。     

风力机叶片裂纹的动态扩展特性研究

首先推导了风力机叶片裂纹尖端塑性区的应力强度因子,确定了影响裂纹扩展的主要因素,然后根据裂纹扩展时的声发射信号特性,探讨了声发射信号的特征提取方法。在试验中分析了不同试件的裂纹形貌特性,并针对采集的裂纹扩展声发射信号,比较了不同小波基函数对声发射信号特征提取的影响,提取了裂纹动态扩展的特征频率,从而建立了声发射特征参数与声发射源之间的关联机制,为风力机叶片状态监测和早期预警提供理论依据。     

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.     

Study of Thin Fluid Film Using Impulsive Load and Its Optimization for Noise Reduction in Forging

To reduce the transmission of impact energy in a drop forge from the impacting die to the anvil while retaining the deformation efficiency, a double-pad oil film has been developed and tested.

The present paper is focused on the behavior of the oil film under an impulsive load. From the experimental apparatus, the measured pressure distribution over the bottom pad area is compared with that of theoretical results for both small and large gaps.

On the basis of the theoretical analysis of the effect of the bearing parameters on the deformation efficiency and emitted sound pressure, the present double-pad oil film bearing is optimized experimentally. From the recorded signals, it is shown that an optimum point does exist where the emitted sound pressure is minimum.     

基于改进的维奥拉积分方法提取心音信号包络

心音是评价人体心脏功能的重要生理信号之一,分析心音信号特征对心脏疾病的早期诊断和治疗具有重要意义。分别采用希尔伯特黄变换、归一化香农能量以及维奥拉积分方法提取心音信号的特征包络,并针对维奥拉积分法存在的不足,结合香农能量方法可抑制高、低强度信号的特性,提出了一种改进的心音信号特征包络提取方法,最后采用阈值法实现了对心音信号的分段。采用改进的维奥拉积分方法对55例心音信号提取特征包络并进行分段处理,经验证该方法能有效的提取心音信号特征包络,对正常信号分段正确率达到了100%,异常信号分段正确率平均达到了95.32%,适合于心音信号分段。     

基于声发射技术的箱型梁大应力区故障模式识别

在介绍了声发射技术的基础上,采用 BP 神经网络,分别对预置故障的港口起重机典型结构———箱型梁的声发射信号进行分析.选用原始波形特征参数以及它们之间组合派生出的特征参数等9个参数作为神经网络的输入,设计箱型梁大应力区常见五种故障模式:正常、局部表面裂纹、局部深埋裂纹、局部焊缝、对接焊缝为最终识别分类模式.结果表明,该方法可以对以上五种模式的声发射源进行有效识别     

基于模糊熵的转子碰摩声发射信号的识别

利用模糊熵理论来度量转子碰摩声发射信号的特征参数相对于不同碰摩状态识别模式的不确定度。根据碰摩声发射信号的特点,选用平均信号电平、幅度、幅度动态范围以及小波包分解信号前四个节点重构信号的能量值作为声发射信号识别的特征参数,由训练样本确定各特征参数针对不同碰摩类别的基于高斯形式的隶属度函数,并由隶属函数得到特征参数与类别之间的模糊关系矩阵。由于各特征参数对于声发射信号识别的有效性不同,因此在计算模糊关系矩阵时引入有效度系数,提出一种利用模糊熵定义有效度系数的方法。结合该系数得到修正的模糊关系矩阵并计算综合评价模糊集合,选择隶属度最大的类别作为识别结果。在转子试验台上采集的不同碰摩状态的声发射信号进行验证,试验结果表明,模糊综合评价方法是一种有效的声发射识别手段,并可以利用参数有效性的差异来提高识别效率。     

Acoustic emission from trabecular bone during mechanical testing: the effect of osteoporosis and osteoarthritis

This study examines the relation between the nature of acoustic emission signals emitted from cancellous bone under compression and the mechanical properties of the tissue. The examined bone specimens were taken from 12 normal, 31 osteoporotic and six osteoarthritic femoral heads. The mechanical behaviour of the osteoporotic bone specimens was found to be significantly different from that of the normal specimens both in the pre-yield and post-yield ranges. In the osteoarthritic bones only the elastic behaviour was significantly different. The rates of acoustic events before yield and beyond it were found to be significantly higher both in the osteoporotic and osteoarthritic bone specimens. The average peak amplitude of the signals was also significantly higher in the diseased bones. Stepwise regression analysis showed that a combination of the acoustic emission parameters could significantly predict some mechanical properties of the bone. The energy absorbed during compression and the ultimate compressive stress of the specimens could be estimated from the rate of pre-yield acoustic events, the average amplitude of the signals and the rate of post-yield events. However, the explanation power of the acoustic emission parameters was only moderate. The nature of acoustic emission signals was thus demonstrated to be a potential tool for assessing bone quality.     

Prediction of metal deposition from arc sound and weld temperature signatures in pulsed MIG welding

The weld deposition efficiency is an important economic factor like productivity and weld quality in gas metal arc welding (GMAW). There is a strong relationship between arc sound signals and arc stability (or deposition efficiency) in GMAW. In this work, the variation of weld deposition efficiency with various pulse parameters in pulsed metal inert gas welding was investigated. The arc sound signal along with current and voltage signals were acquired and analyzed in time domain as well as in frequency domain. The sound signal kurtosis and arc power were found to be highly correlated with welding process stability. The weld deposition efficiency was also related to weld surface peak temperature. Finally, an attempt was made to correlate the sound time domain as well as frequency domain features of sound signal with the deposition efficiency. The variation of pulse shape with the duty factor also influenced the deposition efficiency as evidenced by in fast Fourier transform analysis.     

频带能量特征法在声发射刀具磨损监测系统中的应用

基于对声发射(AE)信号特点的分析和小波包分解理论对不平稳信号特征提取的优势,提出一种利用AE信号的能量变化来监测刀具磨损状态的方法。该方法利用db8小波基对AE信号进行5层小波包分解,将分解后各频带上的能量值作为特征参数,并组成特征向量。分别提取在新刀和刀具磨损状态下的特征向量,根据其变化即可判别刀具磨损的程度。试验结果验证了该方法在刀具磨损判析中的可用性。     

通过声音信号判断切削颤振的试验研究

通过建立高速铣削试验台获取高速铣削声音信号,并对数据进行分析,发现切削过程声音信号的幅频域特性与其稳定性有着紧密的联系.该试验为利用声音信号判断切削颤振提供了事实依据,并为切削颤振控制打下了试验基础.     

声发射监测切屑状态的可行性研究

金属切削过程中产生丰富的声发射。用声发射技术监测切屑状态是一个很有潜力的新方法。本文通过实验研究了车削时切屑状态和声发射信号间的相互关系,结果表明：1.声发射DC包络信号脉冲能准确反映断屑情况;2.声发射加权计数率波形和包络信号均值能反映切屑形状和异常切屑状态;3.不同类型切屑的形成过程具有不同的声发射信号波形。