Seismic signal analysis based on the dual-tree complex wavelet packet transform

We tried to apply the dual-tree complex wavelet packet transform in seismic signal analysis. The complex wavelet packet transform (CWPT) combine the merits of real wavelet packet transform with that of complex continuous wavelet transform (CCWT). It can not only pick up the phase information of signal, but also produce better ″focal- izing″ function if it matches the phase spectrum of signals analyzed. We here described the dual-tree CWPT algo- rithm, and gave the examples of simulation and actual seismic signals analysis. As shown by our results, the dual-tree CWPT is a very effective method in analyzing seismic signals with non-linear phase.

Defects Recognition of 3D Braided Composite Based on Dual-Tree Complex Wavelet Packet Transform

Textile-reinforced composites,due to their excellent highstrength-to-low-mass ratio, provide promising alternatives to conventional structural materials in many high-tech sectors. 3D braided composites are a kind of advanced composites reinforced with 3D braided fabrics; the complex nature of 3D braided composites makes the evaluation of the quality of the product very difficult. In this investigation,a defect recognition platform for 3D braided composites evaluation was constructed based on dual-tree complex wavelet packet transform( DT-CWPT) and backpropagation( BP) neural networks. The defects in 3D braided composite materials were probed and detected by an ultrasonic sensing system. DT-CWPT method was used to analyze the ultrasonic scanning pulse signals,and the feature vectors of these signals were extracted into the BP neural networks as samples. The type of defects was identified and recognized with the characteristic ultrasonic wave spectra. The position of defects for the test samples can be determined at the same time. This method would have great potential to evaluate the quality of 3D braided composites.

Performance Evaluation of Complex Wavelet Packet Modulation (CWPM) System over Multipath Rayleigh Fading Channel

In this paper a novel multicarrier modulation system called Complex Wavelet Packet Modulation (CWPM) has been proposed. It is based on using the Complex Wavelet Transform (CWT) together with the Wavelet Packet Modulation (WPM). The proposed system has been tested for communication over flat and frequency selective Rayleigh fading channels and its performance has been compared with some other multicarrier systems. The simulation results show that the performance of the proposed CWPM system has the best performance in all types of channel considered as compared with OFDM, Slantlet based OFDM, FRAT based OFDM and WPM systems. Furthermore, the proposed scheme has less PAPR as compared with the traditional WPM multicarrier system.

Recognition of Group Activities Using Complex Wavelet Domain Based Cayley-Klein Metric Learning

A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning.Non-sampled dual-tree complex wavelet packet transform(NS-DTCWPT)is used to decompose the human images in videos into multi-scale and multi-resolution.An improved local binary pattern(ILBP)and an inner-distance shape context(IDSC)combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient features.The extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization problem.The group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric learning.Experimental results on behave video set,group activity video set,and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms.

DTCWPT与TSMAE融合的刀具磨损状态辨识方法

获取高质量的刀具磨损特征信息是识别刀具磨损状态的前提。为克服现有刀具磨损状态辨识方法中特征信息提取不足的问题,提出了一种基于双树复小波包变换(dual-tree complex wavelet packet transform,DTCWPT)、时移多尺度注意熵(time-shifted multiscale attention entropy,TSMAE)和随机森林(random forest,RF)的刀具磨损状态辨识方法。利用实测刀具磨损数据集对所提方法的有效性进行了验证,并从信号分解和特征提取两方面与其他磨损辨识技术进行了对比。结果表明,在特征提取阶段,所提方法展现出极高的效率,分别仅需9.41 s和14.91 s即可完成特征提取。在磨损辨识阶段,多次实验的平均辨识精度分别达到了99.33%和100%,充分证明了该方法不仅能够迅速响应,还能准确地辨识刀具的磨损状态。相较其他方法,所提方法在效率和精度上都有明显的优势,在刀具磨损状态辨识领域具有较高的应用潜力。

Satellite Image Adaptive Restoration Using Periodic Plus Smooth Image Decomposition and Complex Wavelet Packet Transforms

A satellite image adaptive restoration method was developed that avoids ringing artifacts at the image boundary and retains oriented features. The method combines periodic plus smooth image decom- position with complex wavelet packet transforms. The framework first decomposes a degraded satellite im- age into the sum of a ＂periodic component＂ and a ＂smooth component＂. The Bayesian method is then used to estimate the modulation transfer function degradation parameters and the noise. The periodic component is deconvoluted using complex wavelet packet transforms with the deconvolution result of the periodic component then combined with the smooth component to get the final recovered result. Tests show that this strategy effectively avoids ringing artifacts while preserving local image details （especially directional tex- tures） without amplifying the noise. Quantitative comparisons illustrate that the results are comparable with previous methods. Another benefit is that this approach can process large satellite images with parallel processing, which is important for practical use.

基于双树复小波包变换的钢丝绳断丝损伤信号特征信息提取研究

针对钢丝绳断丝损伤信号特征信息难以有效提取的问题,提出一种基于双树复小波包变换与奇异值分解相结合的时频域特征信息提取方法。首先将钢丝绳断丝损伤信号采用双树复小波包变换为等长的频带子信号,构造时频域空间状态矩阵,然后采用奇异值方法提取各频带子信号的奇异值,组成表征各类损伤状态的特征向量,得到钢丝绳断丝损伤信号的特征信息矩阵。采用距离可分离性判据与传统时域特征信息提取方法相比较,结果表明双数复小波包变换与奇异值分解相结合的特征信息提取方法具有更强的分类能力。

基于双树复小波包变换和形态学的减速机故障诊断

针对煤机设备减速机的振动信号特征难以提取的问题,提出了双树复小波包变换和形态学相结合的故障特征提取策略。首先利用双树复小波将减速机的振动信号分解成若干个不同频段的分量。然后根据减速机故障特征频率分布特征,选取相关的频段分量进行降噪重构。最后对重构后含有故障特征频率的分量进行形态滤波处理,进而提取出减速机的故障特征。利用现场实测数据进行验证,结果表明,该故障特征提取策略可以有效地提取出减速机的故障特征。

双树复小波包变换语音增强新算法

实小波包变换是语音增强中效果较好的一种算法,利用阈值的方法对小波包系数进行压缩进而重构语音信号。分析了实小波包变换的平移敏感性,以及其对语音进行增强时的缺陷。提出采用双树复小波包变换方法进行语音增强,当低通滤波器和高通滤波器对应的小波基近似为希尔伯特变换对时,该变换能大大减小实小波包变换中的平移敏感性。同时考虑小波包系数之间的相互关系,提出了重叠块复阈值算法。结果表明,算法优于传统实小波包变换及点阈值算法,尤其对含周期噪声的语音信号,双树复小波包变换算法的优势更为明显。

基于双树复小波包变换的滚动轴承故障诊断

针对滚动轴承故障的振动信号具有非平稳特性,存在强烈噪声干扰,难以提取故障特征频率的情况,提出了基于双树复小波包变换阈值降噪的故障诊断方法。首先将非平稳的故障振动信号进行双树复小波包分解,得到不同频带的分量;然后对每个分量求其峭度值和相关系数并进行比较;最后选取峭度值和相关系数较大的分量进行软阈值降噪和双树复小波包重构,即可有效地消除振动信号中噪声的干扰,同时保留信号中的有效信息即实现了故障特征信息的提取。本文对轴承外圈故障试验和实际工程数据进行了相关分析,并对比传统离散小波包降噪的效果,本文方法处理后的信号冲击周期性更好,较理想地去除了噪声的影响,验证了该方法可以有效地去除噪声并提取滚动轴承故障的特征信息。

基于双树复小波包峭度图的轴承故障诊断研究

针对传统包络谱和峭度图分析技术的缺陷,提出一种基于双树复小波包峭度图的轴承故障诊断方法。该方法综合利用了双树复小波包变换和峭度图分析技术,克服了原峭度图方法只采用FIR和短时傅里叶变换滤波器的缺点,提高了从强噪声环境中提取瞬态冲击特征的能力。首先利用双树复小波包变换,将振动信号分解成不同频带的分量,然后计算各小波分量的谱峭度,再利用谱峭度的滤波器作用,计算最大峭度值对应分量信号的包络谱,根据包络谱就可识别齿轮箱轴承的故障部位和类型。齿轮箱轴承故障振动实验信号的研究结果表明:该方法不仅提高了信噪比和频带选择的正确性,而且能有效地识别轴承的故障。

基于时频奇异谱和RVM的柴油机故障诊断研究

提出一种基于双树复小波包时频奇异谱和关联向量机的柴油机故障诊断方法,针对连续小波时频分布计算量大,分析速度慢,运用双数复小波包分解提取柴油机缸盖振动信号中的时频分布特征,并进行奇异值分解,结合柴油机运行的时域特点,通过特征优选,形成组合特征集,输入关联向量机多类分类器,从而实现柴油机的故障诊断。试验结果表明,该方法分析速度快,故障识别效果好。

基于双树复小波包变换能量泄漏特性分析的齿轮故障诊断

为有效利用双树复小波包变换提取齿轮故障特征信息,提出基于双树复小波包能量泄漏特性分析的故障诊断方法。首先根据高斯白噪声频率充满整个频带的特性,通过双树复小波包变换对高斯白噪声进行分解,利用频带能量泄漏的定量分析方法,验证了双树复小波包变换具有较低的频带能量泄漏特性;其次利用双树复小波包变换逐层分解信号,对每层分解所得分量求其FFT谱的峭度,得到基于双树复小波包变换的谱峭度图,根据图中峭度最大的原则,可以自动准确的选择信号分解最佳层数和最佳分量;最后将基于双树复小波包变换的谱峭度图的故障诊断方法应用于实际工程中,对齿轮故障振动信号进行分析,选择最佳分解层数和分量后利用希尔伯特包络解调,有效准确地提取了故障特征信息,验证了方法的可行性和有效性。该研究可为旋转机械设备中齿轮箱故障诊断的故障特征提取提供参考。

基于双树复小波包变换的地震信号分析方法

尝试性地将一种双树复小波包变换方法应用于地震信号分析中 .复小波包变换综合了实小波包变换与连续复小波变换各自的优点 ,不但能提取信号的相位信息 ,而且选取与被分析信号相频特性相匹配的复小波包 ,可以对信号产生更好的“聚焦”作用 .本文描述了一种双树复小波包变换算法 ,并给出了模拟信号及实际地震记录的分析实例 .研究结果表明 ,双树复小波包变换是分析具有非线性相位地震信号的一种较为有效的方法 .

双树复小波包和ICA用于滚动轴承复合故障诊断

针对滚动轴承复合故障信号特征难以分离的问题,提出将双树复小波包变换和独立分量分析(independent component analysis,简称ICA)结合的方法应用到滚动轴承复合故障诊断中。首先,利用双树复小波包变换将复杂的、非平稳的复合故障信号分解为若干不同频带的分量;其次,引入ICA对各个分量所组成的混合信号进行盲源分离,从而尽可能消除频率混叠;最后,对从混合信号中分离出来的独立信号分量进行希尔伯特解调,即可实现对复合故障特征信息的分离和故障识别。试验结果表明,该方法可以有效地分离和提取轴承复合故障的特征频率,验证了方法的可行性和有效性。

双树复小波包和谱峭度在齿轮故障诊断中的应用

针对故障齿轮振动信号的非平稳和调制特性,提出了基于双树复小波包变换和谱峭度的齿轮故障诊断方法.首先,利用双树复小波包变换将原始振动信号分解为若干个不同频带的信号分量,选择与原始信号相关系数大的分量进行阈值降噪并重构;然后,对降噪后的信号利用谱峭度所得的峭度图选择最佳的带宽和频带中心进行相应的带通滤波处理;最后,将带通滤波后的信号作平方包络和傅里叶变换,即可得到信号的包络解调谱,从而提取故障特征信息.通过对试验和工程实际的齿轮故障信号分析表明:双树复小波包变换和谱峭度结合的方法可有效地提取齿轮故障特征信息,进而实现故障识别,验证了方法的可行性和有效性.

基于双树复小波包自适应Teager能量谱的滚动轴承早期故障诊断

针对滚动轴承早期故障特征信息难以识别以及带通滤波器参数设置依赖使用者经验等造成共振带不能有效确定并自适应提取的问题,提出了频带幅值熵的概念。在此基础上,将双树复小波包变换和Teager能量谱结合,提出了基于双树复小波包变换自适应Teager能量谱的早期故障诊断方法。该方法首先利用双树复小波包将采集到的振动信号进行分解,并计算各子带的频带幅值熵。然后将熵值按升序排列后依次作为阈值,提取频带幅值熵大于或等于阈值的子带,依据峭度指标确定最佳熵阈值和双树复小波包最佳分解层数,从而自适应并有效地提取出共振带。最后对共振带进行Teager能量谱分析,即可从中准确地识别出轴承的故障特征频率。通过信号仿真与工程实验数据分析验证了该方法的有效性与优越性。

基于双树复小波包和AR谱的滚动轴承复合故障诊断方法

针对滚动轴承复合故障信号中故障特征难以分离的问题,提出了基于双树复小波包和自回归(autoregressive,AR)谱的故障诊断方法.首先,利用双树复小波包变换将复杂的、非平稳的复合故障振动信号分解为若干个不同频带的分量;然后,对包含故障特征的分量进行希尔伯特包络;最后,对包络信号求其AR功率谱,由此实现对复合故障特征信息的分离和故障识别.实验结果表明:该方法可有效地分离轴承复合故障的特征频率,验证了该方法的可行性和有效性.

基于小波变换的心电信号检测新方法

首先利用正交小波包对心电信号进行去噪,然后采用Mallat算法获得信号在不同尺度上的小波分解细节信号,再对23尺度上的细节信号进行处理,使得与R波对应的模极值大幅度增加,提高了QRS波的检测率.经MIT BIH心电数据库检验,QRS波检测率达到99.86%.

基于DTCWPT和t-SNE的去噪方法及在故障诊断中的应用

为了提取被强噪声淹没的机械设备振动信号中蕴含的微弱故障特征,依据有用信号和噪声在空间分布特性的不同,将流形学习的方法引入到信号降噪中,提出一种将双树复小波包(DTCWPT)和t分布随机近邻嵌入(t-SNE)结合的去噪方法,充分利用了DTCWPT分解的多尺度特性以及t-SNE的非线性降维能力。将振动信号进行双树复小波包分解,依据各尺度小波包系数Shannon熵值搜索最佳小波包基,利用提出的新的阈值函数,对最佳小波包基的小波包系数进行去噪并单支重构组成高维信号空间,然后,采用t-SNE提取高维空间的低维流形,对低维信号序列进一步采用阈值去噪,利用谱回归分析重构回一维信号序列。最后,通过对仿真信号与滚动轴承振动信号进行去噪,结果证实了方法具有良好的非线性去噪性能,将仿真信号的信噪比从-1提高到8.6 d B,并且能更有效的提取强噪声干扰下滚动轴承的故障特征频率。