小波包分形在远区核爆电磁脉冲识别中的应用

针对远区核爆电磁脉冲(NEMP)和闪电电磁脉冲(LEMP)的识别率不能满足实际需求的问题,提出了一种基于小波包分形技术的识别方法。首先,对实测的NEMP和LEMP做插值、归一化等预处理;然后,基于小波包理论对预处理后的信号进行2层小波包分解,并利用小波包系数重构信号的分形维数,组成信号的特征向量;最后,采用最小二乘支持向量机(LSSVM)作为分类器,利用五折交叉验证法选取最优的模型参数,将特征向量输入分类器中进行训练后获得测试结果。实验结果表明,小波包分形方法在NEMP和LEMP的识别上效果显著,平均识别率达到99%以上,具有较高的应用价值。     

基于网格维数的汉语语音分形特征研究

应用分形理论来研究汉语语音信号的分形特征。木文首先在传统盘维数基础上提出了一种等差尺度网格维数算法来快速计算语音信号的分维数;在此基础上,对汉语男女声的２１种声母和３８种韵母语音信号的分维数进行了计算和统计分析,得到了汉语语音分维数的统计分布规律。本文实验结果表明,汉语语音信号具有分形标度不变性,网格维数能够反映语音信号波形的复杂程度。     

船舶辐射噪声的混沌现象研究

水下弱信号的检测和识别是当今水声信号处理领域中存在的难题．应用混沌、分形理论，从根空间轨迹、Lyapunov指数和关联维等方面研究了船舶辐射噪声的混饨现象，发现船舶辐射噪声信号的确存在混沌吸引子，且不同类别的信号具有不同的吸引子维数．这一结果将为水声信号处理，为水下目标探测和识别提供崭新的理论手段。     

用小波和分维数方法处理喘振实验数据

本文针对离心压缩机喘振的实验数据，采用小波分析，发现喘振先兆的频率与后来发生的喘振频率接近，且喘振先兆和喘振均为周期性的脉动．用分维数方法进行处理分析，通过计算压缩机系统不同工况下的关联维数，可以看出关联维数是信号复杂程度的度量．以上工作为实施喘振的主动控制提供了依据．     

彩色纹理分析中的多特征数据融合方法

在小波分解基础上将纹理特征、颜色特征及纹理与颜色的空间相关特征进行融合，提出了一种新颖的彩色纹理特征提取方法，同时结合20类真实彩色自然纹理，针对塔式小波分解(PWD)，不完全树型小波分解(ICTSWD)和小波包分解(WPD)进行了多特征融合和分类比较，实验结果表明：塔式小波分解基础上的多特征融合，其正确分类率为85．78％；小波包分解基础上的多特征融合，其正确分类率为91．03％，但其特征维数呈指数增长；而不完全树型小波分解有选择地进行通道分解，其维数大大下降，多特征融合后的正确分类率达到90．63%，同时也表现出良好的抗噪能力。     

基于小波变换的混沌信号相空间重构研究

应用小波变换和非线性动力学方法研究了混沌信号在相空间中的行为，指出混沌时间序 列的小波变换实质上是在重构的相空间中，混沌吸引子向小波滤波器向量所张的空间中的投 影，与Packard等人提出的相空间重构方法本质上是一致的.实验结果表明，混沌信号经过 小波变换后，吸引子轨迹与原有轨迹具有相似的结构，同时，系统的关联维数、Kolmogorov 熵等非线性不变量仍然得到保留.这些结果表明，利用小波变换研究混沌信号是有效的. 关键词： 小波变换 相空间重构 混沌信号 脑电信号     

时空中的分形

分形过去经常是在几何空间中进行研究，因为它具有几何空间内的标度不变性，即在空间中收缩或放大空间尺度时，其几何形态保持自相似特性，最近西班牙马德里数学与基础物理研究所的C．Escudero博士着重研究在时间中的分形结构，即在不同的时间尺度内，被测量的样本同样存存着标度不变性,分形本身是一种复杂的几何结构，例如一条一维的曲线，让它不断地自相似地弯曲，它就可以成为一个处于一维与二维之间的几何结构，也就是具有“面”的特性，     

气体吸附法测定二氧化硅干凝胶的分形维数

提出了一种方便、科学有效的利用气体吸附法测定二氧化硅干凝胶等多孔材料分形维数（表面分形维数和孔分布分形维数）的方法，不需要进行一系列的吸附/脱附实验，只需要利用单一气体的一次吸附/脱附实验得出的样品孔分布、比表面数据，与不同的标尺进行关联，即可同时获得表面分形维数和孔分布分形维数.通过误差分析和校正，保证了结果的可靠性.用上述方法测定了二氧化硅干凝胶的分形维数，以FHH法和SAXS法对所得结果进行了比较和验证，并对吸附/脱附过程所得结果的差异进行了初步分析. 关键词： 分形维数 气体吸附 二氧化硅 干凝胶     

小波分析在强流直线感应加速器信号处理中的应用

 应用小波变换具有良好的时频局部特性，通过对强流直线感应加速器(LIA)脉冲信号的去噪声、信号突变点检测以及时间间隔测量等处理，表明小波变换在LIA信号处理中有广泛的应用前景；利用小波包分析的每个节点都代表了对应频带的信号特征的特点，对“神龙一号”快脉冲波形数据进行小波包变换，以各频带信号能量为元素构造特征向量，实现了高维波形数据的特征值提取，达到了数据压缩和降维的目的，为进一步实现LIA故障智能诊断、预测维护提供了一种可行的途径。     

单颗金刚石切削无氧铜的声发射关联维特征*

声发射技术可以实现无氧铜切削加工特征的监测与评价。采用声发射技术监测单颗金刚石磨粒旋转切削无氧铜，利用G-P算法重构出声发射时域信号相空间，采用自相关函数法计算出相空间时间延迟参数，通过相空间双对数曲线的计算，得到不同切削工况下的关联维数。研究结果表明，进给速度和切削速度对声发射信号影响较不显著，切深与声发射信号振幅呈正效应关系；声发射信号双对数曲线呈现阶段性增加趋势，并逐渐收敛于饱和状态，关联维数随着嵌入维数的增加先快速下降后趋于平稳；金刚石切削无氧铜的声发射信号具有混沌运动变化特性，在较小嵌入维数时，关联维数与切深和切削速度呈现线性负效应关系，与进给速度呈现线性正效应关系。该研究为无氧铜的切削加工提供理论参考。     

改进粒子滤波在汽轮机故障诊断中的应用夏飞,郝硕涛

针对汽轮机的振动信号容易受到较为复杂的随机噪声污染,提出了一种改进粒子滤波的振动信号降噪方法。首先建立采集振动信号的数学模型,将其作为粒子滤波的状态方程;然后利用小波分析提取采集振动信号的背景噪声,将其和状态信号一起作为观测信号,得到观测方程,把降噪问题转化成在状态空间模型下的滤波问题。由于采用序贯重要性采样的粒子滤波存在着样本退化问题,在重采样阶段采用了一种权值排序、优胜劣汰的重采样算法,就是对各粒子的归一化权值从小到大的排列顺序,并根据权值方差大小淘汰粒子,从而得到了改进的粒子滤波算法,在一定程度上解决了标准粒子滤波的退化问题。进而运用改进粒子滤波算法对振动信号进行降噪处理,降噪前信号和降噪后信号分别通过小波包分解系数求取频带能量,根据各个频带能量的变化提取故障特征向量浓缩了汽轮机振动故障的全部信息,对提取的故障特征向量应用诊断识别算法进行故障模式识别。通过对比降噪前信号和降噪后信号的故障诊断识别率,证明了改进粒子滤波在汽轮机故障诊断中的应用效果更佳。     

A multiscale noise tuning stochastic resonance for fault diagnosis in rolling element bearings

Condition monitoring of rotating machinery is important to extend the mechanical system's reliability and operational life. However, in many cases, useful information is often overwhelmed by strong background noise and the defect frequency is difficult to be extracted. Stochastic resonance (SR) is used as a noise-assisted tool to amplify weak signals in nonlinear systems, which can detect weak signals of interest submerged in the noise. The multiscale noise tuning SR (MSTSR), which is originally based on discrete wavelet transform (DWT), has been applied to identify the fault characteristics and has also increased the signal-to-noise ratio (SNR) improvement of SR. Therefore, a novel tri-stable SR method with multiscale noise tuning (MST) is proposed to extract fault signatures for fault diagnosis of rotating machinery. The wavelet packets transform (WPT) based MST can obtain better denoising effect and higher SNR of resonance output compared with the traditional SR method. Thus the proposed method is well-suited for enhancement of rotating machine fault identification, whose effectiveness has been verified by means of practical vibration signals carrying fault information from bearings. Finally, it can be concluded that the proposed method has practical value in engineering.     

基于HHT变换的起动电动机特征信号时频分析

针对车辆起动电动机电气和机械故障发生时特征信号的时变不平稳特性,进行了时频域分析处理,提出了利用现代信号处理方法对故障信号提取特征向量的方法,主要对起动电动机的电枢和轴承故障进行诊断。在构建电机故障测试实验平台的基础上,利用破坏性实验构造了故障类型,测取了电枢电流和振动信号,分别采用小波分析理论和HHT变换对信号进行分析,通过分解再重构的方式将信号分解成了频率由高到低的不同分量,并获得了故障的特征频率,提取了特征向量。实验结果表明,基于HHT变换的现代信号处理方法在处理时变非平稳信号方面比小波分析理论更具有自适应性,更易识别。     

Some optimizations on detecting gravitational wave using convolutional neural network

This work investigates the problem of detecting gravitational wave (GW) events based on simulated damped sinusoid signals contaminated with white Gaussian noise. It is treated as a classification problem with one class for the interesting events. The proposed scheme consists of the following two successive steps: decomposing the data using a wavelet packet, representing the GW signal and noise using the derived decomposition coefficients; and determining the existence of any GW event using a convolutional neural network (CNN) with a logistic regression output layer. The characteristic of this work is its comprehensive investigations on CNN structure, detection window width, data resolution, wavelet packet decomposition and detection window overlap scheme. Extensive simulation experiments show excellent performances for reliable detection of signals with a range of GW model parameters and signal-to-noise ratios. While we use a simple waveform model in this study, we expect the method to be particularly valuable when the potential GW shapes are too complex to be characterized with a template bank.     

一种多算法融合的实时滤波在光纤陀螺中的应用

根据光纤陀螺输出信号的特点和应用环境的要求,在Mallat小波变换的基础上,研究了一种多算法融合的实时滤波算法.该算法在光纤陀螺刚启动,数据量偏少时,通过IIR滤波器进行滤波|采样数据量足够多时,通过施加滑动数据窗来实现小波实时去噪,采用周期对称延拓的方法去除小波去噪的边界问题,可有效去除光纤陀螺输出信号中高频部分的噪音,提高滤波效果,抑制陀螺的随机漂移.通过实验验证了该方法对陀螺输出信号进行滤波的可行性和有效性.     

Extraction of weak crack signals based on sparse code shrinkage combined with wavelet packet filtering

Early crack signals in critical infrastructure components of major equipment are hardly to be extracted due to its low signal noise ratio (SNR). A de-noising method combined wavelet packet (WP) technology with sparse code shrinkage (SCS) is proposed in this study. Firstly, WP reconstruction technology is used to reserve the crack signal with a specified frequency range. That is, the signal is decomposed by Meyer wavelet into five layers, and the signal with the frequency range from 187.5 kHz to 609.375 kHz is reserved. Then SCS method removes noise within the specified frequency range. Namely, the probability density function (PDF) of the signal independent coefficients is estimated via the generalized Gaussian model (GGM) in the independent component analysis (ICA) space. The nonlinear de-noising is finished by utilizing maximum a posteriori (MAP) estimate. The results obtained by the combined method are compared with those generated by the SCS method and the WP de-noising method. It demonstrates that the combined method is the best one among the three methods in extracting weak signals. Its output SNR is −2.38 dB and the correlation coefficient (CC) is 0.54 when the input SNR is −20 dB. They are higher than those obtained by the SCS method (SNR −4.46 dB and CC 0.51). The WP method is the worst (SNR −3.54 dB and CC −0.003). Therefore, the combined method is quite suitable for weak signal extraction.     

小波尺度相关滤波及其改进算法在电火花声源信号处理中的应用

分析提取宽带电火花声源信号,受到船舶辐射噪声的严重干扰,在5 kHz以下的能量集中区信噪比低,且二者Lipschitz指数特性相近,传统基于相邻尺度相关的滤波算法抗干扰能力有限。根据宽带电火花声源信号不同频带所受干扰的不同,信噪比较高的小尺度高频小波系数,采用相邻尺度相关的滤波算法;信噪比较低的大尺度中低频小波系数,采用跨尺度相关的滤波算法,并对算法中阈值系数的选取方法进行修正。结果表明,该算法滤波效果良好,有效的提取了电火花声源信号,适合窄带强干扰背景噪声下的宽带水声信号处理。      

Complex signal analysis for wind turbine planetary gearbox fault diagnosis via iterative atomic decomposition thresholding

The vibration signals from complex structures such as wind turbine (WT) planetary gearboxes are intricate. Reliable analysis of such signals is the key to success in fault detection and diagnosis for complex structures. The recently proposed iterative atomic decomposition thresholding (IADT) method has shown to be effective in extracting true constituent components of complicated signals and in suppressing background noise interferences. In this study, such properties of the IADT are exploited to analyze and extract the target signal components from complex signals with a focus on WT planetary gearboxes under constant running conditions. Fault diagnosis for WT planetary gearboxes has been a very important yet challenging issue due to their harsh working conditions and complex structures. Planetary gearbox fault diagnosis relies on detecting the presence of gear characteristic frequencies or monitoring their magnitude changes. However, a planetary gearbox vibration signal is a mixture of multiple complex components due to the unique structure, complex kinetics and background noise. As such, the IADT is applied to enhance the gear characteristic frequencies of interest, and thereby diagnose gear faults. Considering the spectral properties of planetary gearbox vibration signals, we propose to use Fourier dictionary in the IADT so as to match the harmonic waves in frequency domain and pinpoint the gear fault characteristic frequency. To reduce computing time and better target at more relevant signal components, we also suggest a criterion to estimate the number of sparse components to be used by the IADT. The performance of the proposed approach in planetary gearbox fault diagnosis has been evaluated through analyzing the numerically simulated, lab experimental and on-site collected signals. The results show that both localized and distributed gear faults, both the sun and planet gear faults, can be diagnosed successfully.     

混沌干扰中基于同步挤压小波变换的谐波信号提取方法

针对混沌干扰背景下多个谐波信号的提取问题, 提出了一种基于同步挤压小波变换(SST)的谐波信号抽取方法. 首先利用SST将混沌信号和谐波信号组成的混合信号分解为不同的内蕴模态类函数, 然后利用Hilbert变换对分离出的内蕴模态类函数进行频率识别, 从中分离出各谐波信号. 以Duffing混沌背景为例, 对混沌干扰下多谐波信号的提取进行了实验分析. 实验结果表明: 对于不同频率间隔的多个谐波分量, 本文方法的提取结果都具有较高的精度, 而且所提方法对高斯白噪声的干扰具有较好的鲁棒性, 综合提取效果优于经典的经验模态分解方法.