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.

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.

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.

Analysis of Energy Characteristics in the Process of Freak Wave Generation

The energy characteristics in the evolution of the wave train are investigated to understand the inherent cause of the freak wave generation. The Morlet wavelet spectrum method is employed to analyze the numerical, laboratory and field evolution data of this generation process. Their energy distributions and variations are discussed with consideration of corresponding surface elevations. Through comparing the energy characteristics of three cases, it is shown that the freak wave generation depends not only on the continuous transfer of wave train energy to a certain region where finally the maximum energy occurs, but also on the distinct shift of the converged energy to high-frequency components in a very short time. And the typical energy characteristics of freak waves are also given.

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。

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WP-ICA分析的涡旋压缩机故障特征提取方法研究

针对涡旋压缩机故障特征难以提取以及在小样本下故障类别难以区分的问题,提出了一种基于小波包能量谱与独立成分分析相结合的故障特征提取方法,结合支持向量机建立故障分类模型,实现非平稳信号的准确识别。首先对信号进行小波包分解与重构,通过重构系数获取分解后不同频带所对应的小波包能量作为故障特征值;再利用独立成分分析法对故障特征值进行优化得到特征向量值;最后构造支持向量机,输入故障特征向量进行训练和测试,可得出涡旋压缩机故障诊断的准确率。实验和仿真分析结果验证了该方法的有效性,其诊断准确率可达94.5%。

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。

基于振动信号的变压器故障分析

变压器振动信号可以分析出变压器的运行状态,变压器故障的提前预警可以为电网的安全稳定运行提供保障。基于振动信号的小波包分析,研究了变压器不同运行状态的特点。通过分析可知,变压器发生故障时,振动信号分解后的各频谱信号波高频部分所占的比例比正常时增加了一些,而低频部分所占的比例减少了一些。

单轴压缩下正交型交叉裂隙砂岩的破坏前兆试验研究

为探究单轴压缩下正交型交叉裂隙砂岩的破坏前兆信息,开展正交型交叉裂隙砂岩的单轴压缩声发射试验,分别从声发射振铃计数率的临界慢化特征、主频与平均频率质心的演化、小波包能谱特征三个角度进行研究。研究发现试样加载至临近峰值应力时均表现出明显的临界特征:声发射振铃计数率呈“双峰特征”、累积振铃计数急剧攀升;振铃计数率的自相关系数上升到较高的值发生大幅度波动、方差发生量级式“增加-降低-增加”的变化;低频声发射信号数量明显增加,平均频率质心则表现为“先增后减”式变化;对声发射波形信号进行4层小波包分解,3^(#)~16^(#)频带能量占比呈“明显下降-波动式上升”式变化;三种破坏前兆识别方法所得到的前兆响应系数(precursor response coefficient,PRC)的大小顺序依次为:临界慢化特征>频域特征>小波能谱特征,前兆响应应力(precursor response stress,PRSR)的大小顺序与前者相反;随着主裂隙与轴向应力之间夹角的增大;随着主裂隙与轴向应力夹角的增大,三种破裂前兆识别方法所识别前兆点出现时间均延迟。

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。

Wear State Recognition of Drills Based on K-means Cluster and Radial Basis Function Neural Network

Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, decomposing cutting torque components from the original signals by wavelet packet decomposition （WPD）; second, extracting wavelet coefficients of different wear states （i.e., slight, normal, or severe wear） with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network （RBFNN）. The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.

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%。

高速铁路车轮扁疤激扰响应的小波包分解与限值研究

扁疤是轨道交通车辆车轮踏面的典型故障之一,其对于列车运行的平稳性和安全性有很大影响。目前尚未对不同速度下的扁疤限值制定统一的标准。本文对动力学仿真计算得到的轮轨力随机响应采用一种全局性的小波包分解处理方法,提取轮轨力随机信号的能量系数作为评判指标,从能量特征的角度研究了不同速度等级下的车轮扁疤安全限值,并与在时域信号中提取轮轨力随机响应的最大值、均值和脉冲因子作为评判扁疤安全限值的方法相比较。结果表明:小波包能量系数从全局性角度描述轮轨力特性,节点能量系数呈线性规律,可作为高速铁路车轮扁疤安全限值的评判指标。研究得出当列车速度在150~250 km/h范围内,扁疤长度应控制在30 mm以内;当列车速度在250~350 km/h范围内,扁疤长度应控制在25 mm以内。

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。

基于支持向量机的多回路关口电能表在线监测方法研究

根据多回路关口电能表监测的多通道采样连续性,采样信号的多特征及其耦合性,提出一种基于支持向量机的多回路关口电能表在线监测方法,该方法分别设计了基于监测系统的信号采集单元、数据通信单元、数据处理单元及上位机显示单元,搭建了电能表的多个故障缺陷模型,通过信号采集单元将缺陷模型的三相电流及电压实时数据传送给数据处理单元,数据处理单元对收集的数据进行基础运算后形成对应的有功功率样本数据,并对其进行小波包能量谱离线分析后形成故障特征池,构建了基于支持向量机算法的多分类支持向量机模型并将得到的模型内嵌于上位机显示单元,监测系统运行时上位机显示单元直接基于模型的关联规则,与转换后的采样信息进行特征匹配,实现对电能表运行工况的实时监测。经实例验证结果表明该方法能够实时、准确的对多回路关口电能表进行在线监测,具有较强的应用价值及前景。

Environmental variability study on the measured responses of Runyang Cablestayed Bridge using wavelet packet analysis

The structural damage alarming method based on wavelet packet energy spectrum (WPES) for long-span cable-stayed bridges is presented through combination of ambient vibration test and wavelet packet analysis. The environmental variability in the measured WPES and damage alarming indices ERVD of the Runyang Cable-stayed Bridge are discussed in detail using the wavelet packet analysis of the measured acceleration responses of the bridge under daily environmental conditions. The analysis results reveal that the actual environmental conditions including traffic loadings, environmental temperature and typhoon loadings have remarkable correlations with the measured WPES. The changes of environmental temperature have a long-term trend influence on the WPES, while the influences of traffic and typhoon loadings on the measured WPES of the bridge present instantaneous changes because of the nonstationary properties of the loadings. The analysis results of the measured responses further reveal that the damage alarming indices ERVD can sensitively reflect the influences of environmental temperature and typhoon loadings on the dynamic properties of Runyang Cable-stayed Bridge. Therefore, the proposed structural damage alarming indices ERVD under ambient vibrations are suitable for real-time damage alarming for long-span cable-stayed bridges.