Application of the wavelet packet and its energy spectrum to identify nugget splash during the aluminum alloys spot welding

Nugget splash during aluminum alloys spot welding has a detrimental effect on weld nugget integrity, strength and durability of the welded joints. This investigation is performed to identify nugget splash from voltage signals because these are easily accessible on-line. In the present work, we propose a novel method based on the wavelet packet transform and its energy spectrum for pattern recognition of splash signal. The result demonstrates that this novel method is more accuracy and a useful way of monitoring the spot welding quality.

Application of wavelet packet decomposition and its energy spectrum on the coal-rock interface identification

The theory and method of wavelet packet decomposition and its energy spectrum dealing with the coal rock Interface Identification are presented in the paper. The characteristic frequency band of the coal rock signal could be identified by wavelet packet decomposition and its energy spectrum conveniently, at the same time, quantification analysis were performed. The result demonstrates that this method is more advantageous and of practical value than traditional Fourier analysis method.

A Cell Condition-Sensitive Frequency Segmentation Method Based on the Sub-Band Instantaneous Energy Spectrum of Aluminum Electrolysis Cell Voltage

Cell voltage is a widely used signal that can be measured online from an industrial aluminum electrolysis cell.A variety of parameters for the analysis and control of industrial cells are calculated using the cell voltage.In this paper,the frequency segmentation of cell voltage is used as the basis for designing filters to obtain these parameters.Based on the qualitative analysis of the cell voltage,the sub-band instantaneous energy spectrum(SIEP)is first proposed,which is then used to quantitatively represent the characteristics of the designated frequency bands of the cell voltage under various cell conditions.Ultimately,a cell condition-sensitive frequency segmentation method is given.The proposed frequency segmentation method divides the effective frequency band into the[0,0.001]Hz band of lowfrequency signals and the[0.001,0.050]Hz band of low-frequency noise,and subdivides the lowfrequency noise into the[0.001,0.010]Hz band of metal pad abnormal rolling and the[0.01,0.05]Hz band of sub-low-frequency noise.Compared with the instantaneous energy spectrum based on empirical mode decomposition,the SIEP more finely represents the law of energy change with time in any designated frequency band within the effective frequency band of the cell voltage.The proposed frequency segmentation method is more sensitive to cell condition changes and can obtain more elaborate details of online cell condition information,thus providing a more reliable and accurate online basis for cell condition monitoring and control decisions.

Theoretical research on structural damage alarming of long-span bridges using wavelet packet analysis

The state equation and observation equation of the structural dynamic systems under various analysis scales are derived based on wavelet packet analysis. The time-frequency properties of structural dynamic response under various scales are further formulated. The theoretical analysis results reveal that the wavelet packet energy spectrum （WPES） obtained from wavelet packet decomposition of structural dynamic response will detect the presence of structural damage. The sensitivity analysis of the WPES to structural damage and measurement noise is also performed. The transfer properties of the structural system matrix and the observation noise under various analysis scales are formulated, which verify the damage alarming reliability using the proposed WPES with preferable damage sensitivity and noise robusticity.

Generation of artificial earthquakes for matching target response unsmooth spectrum via wavelet package transform

The seismic records of target response spectrum used in the time-history analysis should be allowed to meet the norms. However, the previous fitting methods of target spectrum are mostly for the situations that the target spectrum is a smooth curve. In many cases, it needs to match unsmooth target spectrum for single determined response spectrum. An adjustment of time history via wavelet packet transform was presented, which is able to fit unsmooth target spectrum. It was found that there is a certain bias between the band center frequency of the component of seismic record after wavelet packet decomposition and the peak frequency of response spectra of wavelet packet components. For this reason, five strategies were presented to select iteration points, and the effects of the five strategies were compared with two calculation examples. It was turned out that the peak frequency of the response spectrum of wavelet packet component can lead to good fitting effect when it is selected as the iteration point. In the iteration process, it shows great promise in fitting non-smooth target spectrum and has a trend of converge.

Structural health monitoring of long-span suspension bridges using wavelet packet analysis

During the service life of civil engineering structures such as long-span bridges, local damage at key positions may continually accumulate, and may finally result in their sudden failure. One core issue of global vibration-based health monitoring methods is to seek some damage indices that are sensitive to structural damage, This paper proposes an online structural health monitoring method for long-span suspension bridges using wavelet packet transform （WPT）. The WPT- based method is based on the energy variations of structural ambient vibration responses decomposed using wavelet packet analysis. The main feature of this method is that the proposed wavelet packet energy spectrum （WPES） has the ability to detect structural damage from ambient vibration tests of a long-span suspension bridge. As an example application, the WPES-based health monitoring system is used on the Runyang Suspension Bridge under daily environmental conditions. The analysis reveals that changes in environmental temperature have a long-term influence on the WPES, while the effect of traffic loadings on the measured WPES of the bridge presents instantaneous changes because of the nonstationary properties of the loadings. The condition indication indices VD reflect the influences of environmental temperature on the dynamic properties of the Runyang Suspension Bridge. The field tests demonstrate that the proposed WPES-based condition indication index VD is a good candidate index for health monitoring of long-span suspension bridges under ambient excitations.

联合小波-频域变换的自适应能量检测

针对传统能量检测方法在频谱感知领域中极易受低信噪比环境干扰,忽视可用频谱的定位亦会影响频谱状态的判别结果,提出了一种联合小波-频域变换的自适应能量检测方法,旨在提高能量检测的噪声灵敏度和判别精确度。通过离散小波包变换对信号进行分解并计算子带能量;结合能量范数降低自适应阈值的计算复杂度,以便与子带能量比较;采用快速傅里叶变换定位可用频谱范围。对该方法进行模拟仿真,探究自适应阈值与不同性能参数之间的变化关系。仿真结果表明,该方法具有良好的环境适配性与系统稳定性,且在不同信噪比环境下的检测误差更小。此外,对子带信号进行频域分析以实现归一化频率范围的重新排序,进一步提高了频谱感知的准确度。

一种适用于混合双极直流输电的纵联方向保护方法

直流输电线路通常是以大电感和直流滤波器作为边界,故障行波在边界发生折射和反射时,其行波能量也会因为电感和滤波器的存在而被吸收,导致区内、外高频行波能量产生差异.当故障发生为正方向故障时,其高频前行波能量小于高频反行波能量;当故障发生为反方向故障时,其高频前行波能量大于高频反行波能量.由此特征提出了一种基于前、反行波高频能量差异的纵联方向保护,该保护采用小波包变换提取行波的高频能量,利用前行波与反行波在区内故障和区外故障时的高频能量差异判别故障.仿真结果表明,该保护能快速准确地识别故障,并且不受故障类型、过渡电阻以及故障发生位置距离和噪声的影响,具有一定的实用性.

基于WPES与MEEMD的船用主机振动研究

为揭示船用长冲程低速柴油机健康状态下的振动特征,采用小波包能量谱(Wavelet Packet Energy Spectrum, WPES)和改进的总体平均经验模态分解(Modified Ensemble Empirical Mode Decomposition, MEEMD)结合的特征提取方法,对典型推进工况下低速机的表面振动信号进行3层小波包分解和重构。通过对能量占比较大的节点采用MEEMD方法进行分解,获得IMF1分量频谱。研究结果表明,在40%以下的较低发动机负荷时,各单次燃烧循环的振动波动较小,振动幅值基本一致。提升至50%以上发动机负荷时,燃烧引起振动波动明显增强。50%工况下,中高频能量占总能量的41.51%,为主要振动源。

基于压电传感器的混凝土空洞缺陷发展与修复监测试验研究

为监测混凝土结构服役期间其空洞缺陷的发展情况、缺陷修复后混凝土的缺陷程度并判定相应的抗压强度,探索一种基于应力波的监测方法:将两枚压电传感器粘贴在混凝土试件表面,一枚作为致动器,另一枚作为传感器,利用信号源产生电压信号激励致动器产生应力波,同时测量试件在不同空洞缺陷直径下及缺陷修复后传感器的输出电压,引入输出电压的特征值,之后测试相应试块的抗压强度,建立特征值与混凝土抗压强度间的定量关系评价混凝土力学性能。通过有限元模型研究了混凝土在不同直径的空洞缺陷下应力波的传播特性,证明了幅值与相位表征混凝土缺陷程度的可行性,由此提出基于波飞行时间的损伤参数和基于幅值的能量指数评价混凝土缺陷状态。最后设计了混凝土空洞缺陷发展与修复监测试验,采用扫频信号和脉冲信号对试块进行监测并提取特征值(能量指数、损伤参数)评价混凝土缺陷修复前后的缺陷状态及抗压强度,验证了该方法的可行性并给出了不同激励信号的适用条件。

强震区抗滑桩加固碎石土边坡动力响应特征与桩身损伤识别:以川青铁路九寨沟双线大桥附近工点为例

为掌握西南强震区服役抗滑桩的地震响应特征与损伤状态,基于川青铁路位于强震区典型工点,构建了地震作用下不同损伤状态抗滑桩加固碎石土边坡三维有限差分模型,研究了坡体的加速度响应、抗滑桩结构内力变化以及桩身初始损伤对坡体动力响应的影响;引入小波包能量谱法,基于桩身加速度响应数据,构建能量损伤判别指标对桩身进行损伤状态、损伤位置以及损伤程度的识别。结果表明,抗滑桩能有效降低桩身周围土体的加速度响应,坡体加速度总体表现为高程放大效应,滑面附近是桩身受力集中区域且桩身损伤会导致其抗弯、抗剪承载力下降,侧向水平位移增大,进而加剧坡体加速度响应。损伤判别指标D PERV沿桩身的分布特征能够有效识别抗滑桩的损伤位置和损伤程度,建立的桩顶残余位移、桩顶损伤指标D PERV与损伤程度三者之间的定量关系能够评估桩身相对损伤程度,为边坡工程中抗滑桩服役性能评价提供新的思路和方法。

基于多频带多特征融合的配电网单相接地故障选段方法

针对现有配电网单相接地故障选段方法存在的通信负担重、阈值设置困难及故障特征量单一等问题,提出一种基于多频带多特征融合的配电网单相接地故障选段方法。首先,利用小波包变换分别对故障后首半个工频周期的零序电压导数和零序电流进行分解和重构,得到原始波形的不同频带分量。其次,根据不同频带分量间的极性关系分别引入伏安特性特征向量与零序功率累加和作为故障特征。最后,筛选特征频带并构造基于特征频带与故障特征融合的选段判据。仿真实验及现场数据验证结果表明,所提方法能够有效实现单相接地故障免阈值就地选段。

基于小波包奇异谱熵和WOA-SVM的GIS放电故障诊断

为实现气体绝缘开关设备(GIS)放电故障诊断并提高诊断正确率,提出了一种基于小波包奇异谱熵和鲸鱼优化算法优化支持向量机(WOA-SVM)的GIS放电故障诊断方法。首先,提取GIS放电时的特高频信号的小波包奇异谱熵作为特征向量;然后,采用WOA寻优找到SVM的最优参数,建立准确的分类模型;最后,通过试验模拟GIS典型的放电故障,采用网格搜索参数的SVM、粒子群优化参数的SVM以及所提的WOA-SVM三种算法对GIS放电故障类型进行识别。结果表明所提的WOA-SVM算法故障识别正确率更高、适应度更好且收敛速度更快。

基于小波包能量谱和ELM的光伏逆变器多故障在线诊断

以三电平光伏逆变器作为研究对象,基于小波包能量谱基数对其故障情况提出一种快速诊断方式。在小波包技术下对三电平逆变器桥臂电压及相应信号产生的能量谱特征向量进行分解分析,随后在极端学习机的诊断下,对其内部单或多个器件故障模式进行分析并排除。研究表明,提出的小波包能量谱技术具有较强的技术优越性,相比于传统模式下的BP神经网络以及最小二乘法故障诊断方式,该方式对于检测信号的获取更为容易,拥有较高的抗干扰性,其诊断速度较快,对于故障能够以精确的方式得到原因,从而显著降低故障诊断成本,是较为适宜的在线诊断方式。

Research on seasonal correlation of wavelet packet energy spectrum and temperature of Runyang Suspension Bridge

In order to establish an environmental-condition-normalized structural damage alarming method, the seasonal correlation analysis of wavelet packet energy spectrum (WPES) and temperature of Runyang Suspension Bridge is performed by means of the 236-day health monitoring data. The analysis results reveal that the measured WPES has remarkable seasonal correlation with the environmental temperature. The seasonal change of environmental temperature accounts for the variation of the damage alarming parameter I p of the dominant frequency bands with an averaged variance of 200%. The statistical modeling technique using a 6th-order polynomial is adopted to formulate the correlation between the WPES and temperature, on the basis of which the abnormal changes of measured damage alarming parameter I p are detected using the mean value control chart. It is found that the proposed method can effectively eliminate temperature complications from the time series of WPES and exhibit good capability for detecting the damage-induced 10% variances of the damage alarming parameter I p . And the proposed WPES-based method is superior the modal frequency and hence is more suitable for online real-time damage alarming for long-span bridges.

基于EMD-AR谱频带能量特征的故障特征提取方法研究

通过将经验模态分解(EMD)和AR功率谱参数估计模型相结合,提出一种基于EMD-AR谱估计提取频带能量特征的方法。对振动信号进行经验模态分解,并将分解后能量占比较大的前几组固有模态函数分别进行AR谱估计得到EMD-AR谱曲线;按照频带对EMD-AR谱曲线分段并分别对每一段的能量进行求和,归一化后作为特征值。搭建齿轮箱故障试验平台,采集振动信号进行EMD-AR谱频带能量特征提取,并将所得特征值输入到SVM中进行训练和诊断。结果表明:该特征提取方法结合SVM所得到的故障诊断模型具有较高的识别准确率,能够有效、准确地进行诊断识别。

基于Kriging模型和小波包能量谱的随机模型修正

针对随机模型修正精度和效率低的问题,提出一种基于Kriging模型和小波包能量谱的随机有限元模型修正方法。首先,假设模型待修正参数和响应特征均服从正态分布,将不确定性的模型修正转化为均值和标准差的修正;其次,将待修正参数作为Kriging模型输入,加速度频响函数经过小波包分解后提取的结点能量作为输出,引入政治优化算法优化相关系数以构造Kriging模型;然后,将最小化试验响应与预测响应之差的绝对值作为修正均值的目标函数,最小化交叉熵作为修正标准差的目标函数,通过政治优化算法先后修正参数均值和标准差;最后,以空间桁架结构为例,选取弹性模量和密度为待修正参数验证该方法的可行性。结果表明,所提方法能够有效地修正结构参数均值和标准差,修正后的参数均值、标准差的误差分别低于0.1%、3.5%。

基于小波能量谱和SSA-GRU的混合直流输电系统故障测距方法

针对混合直流输电系统故障测距存在行波波头难以识别以及固有主频不易提取的问题,提出一种基于小波能量谱和麻雀搜索算法(sparrow search algorithm, SSA)优化的门控循环单元(gate recurrent unit, GRU)模型的故障测距方案。首先,分析频谱能量与故障距离的相关关系,利用小波包分解提取小波包能量谱特征向量,作为GRU模型输入。其次,搭建和训练GRU模型,挖掘时间序列中的深层次故障信息,并利用SSA的迭代寻优对GRU模型参数进行优化,实现故障距离的快速准确定位。最后,在PSCAD/EMTDC中搭建混合三端直流输电系统模型,实验结果证明该方法定位精度高、抗干扰能力和泛化能力强,并具有一定的耐过渡电阻能力。

基于小波包能量谱与改进BP神经网络的铁路扣件松脱检测算法研究

为实现铁路扣件在服役过程中松脱位置及松脱程度的准确检测,提出一种基于小波包能量谱与改进BP神经网络的铁路扣件松脱检测算法。首先,参照现行规范,在实地设计了7种铁路扣件不同松脱工况,并依次采集各工况下所对应的钢轨垂向振动加速度信号;随后,对采集到的钢轨垂向振动加速度信号进行7层dB40小波包分解,获取各工况下对应的小波包节点能量比数据信息,在此基础上,从距离和维度两个角度出发,设计了一个向量相异系数指标(VDC)用以实现松脱扣件的定位;此外,依据数据特征,采用粒子群优化算法对BP神经网络进行改进,构建PSO-BP铁路扣件松脱程度检测模型,并进行参数敏感性分析。研究结果表明:健康状态下铁路扣件的VDC最小值为0.17,最大值为0.41,明显小于各松脱状态下铁路扣件的VDC值,据此,可实现对单个和多个松脱扣件的准确定位;构建的PSO-BP铁路扣件松脱程度检测模型可以实现扣件松脱程度的准确检测,且当隐含层中神经元数目设置为20时,模型检测效果最优,对应识别准确率为98.66%。

基于小波包能量谱和希尔伯特变换的管道泄漏检测技术

为了及时检测输油管道泄漏缺陷以减少输油泄漏事故的发生,首先,提出了一种基于小波包(WPT)能量谱和希尔伯特变换(Hilbert)的输油管道泄漏检测技术,改变阀门开度模拟不同泄漏程度的管道泄漏;其次,选取小波包阈值对采集的信号去噪,获得实际有效信号,并用WPT能量谱变换将有效信号分解为频段能量谱,过滤掉不含泄漏信息的频段,保留有效频段;最后,利用Hilbert进行有效频段的谱分析,提取泄漏源信息,实现对管道泄漏工况的诊断分类。结果表明:输油管道泄漏检测平均误差在3%以内,诊断准确率达到95%,与直接Hilbert变换方法以及单纯WPT能量谱分析法相比,定位精度明显提高。