Gearbox Fault Diagnosis using Adaptive Zero Phase Time-varying Filter Based on Multi-scale Chirplet Sparse Signal Decomposition

When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.

Multi-scale spatial relationships between soil total nitrogen and influencing factors in a basin landscape based on multivariate empirical mode decomposition

The relationships between soil total nitrogen(STN)and influencing factors are scale-dependent.The objective of this study was to identify the multi-scale spatial relationships of STN with selected environmental factors(elevation,slope and topographic wetness index),intrinsic soil factors(soil bulk density,sand content,silt content,and clay content)and combined environmental factors(including the first two principal components(PC1 and PC2)of the Vis-NIR soil spectra)along three sampling transects located at the upstream,midstream and downstream of Taiyuan Basin on the Chinese Loess Plateau.We separated the multivariate data series of STN and influencing factors at each transect into six intrinsic mode functions(IMFs)and one residue by multivariate empirical mode decomposition(MEMD).Meanwhile,we obtained the predicted equations of STN based on MEMD by stepwise multiple linear regression(SMLR).The results indicated that the dominant scales of explained variance in STN were at scale 995 m for transect 1,at scales 956 and 8852 m for transect 2,and at scales 972,5716 and 12,317 m for transect 3.Multi-scale correlation coefficients between STN and influencing factors were less significant in transect 3 than in transects 1 and 2.The goodness of fit root mean square error(RMSE),normalized root mean square error(NRMSE),and coefficient of determination(R2)indicated that the prediction of STN at the sampling scale by summing all of the predicted IMFs and residue was more accurate than that by SMLR directly.Therefore,the multi-scale method of MEMD has a good potential in characterizing the multi-scale spatial relationships between STN and influencing factors at the basin landscape scale.

Discrete Wavelet Multi-scale Decomposition of the Temporal Gravity Variations in North China

On the basis of the absolute and relative gravity observations in North China,spatial dynamic variation of regional gravity fields is obtained. A multi-scale decomposition technique is used to separate anomalies at different depths,and give some explanation to gravity variation at different time space scales. Gravity variation trends in North China are improved. Based on this result and the analysis of wavelet power spectrum,the images of the depth of wavelet approximation and detail are obtained. The results obtained are of scientific significance for the deep understanding of potential seismic risk in North China from gravity variations in different time space scales.

The Spatial Equivalence Between Wavelet Decomposition and Phase Space Embedding of EEG

Using both the wavelet decomposition and the phase space embedding, the phase trajectories of electroencephalogram (EEG) is described. It is illustrated based on the present work,that is,the wavelet decomposition of EEG is essentially a projection of EEG chaotic attractor onto the wavelet space opened by wavelet filter vectors, which is in correspondence with the phase space embedding of the same EEG. In other words, wavelet decomposition and phase space embedding are equivalent in methodology. Our experimental results show that in both the wavelet space and the embedded space the structure of phase trajectory of EEG is similar to each other. These results demonstrate that wavelet decomposition is effective on characterizing EEG time series.

Denoising of seismic data via multi-scale ridgelet transform

Noise has traditionally been suppressed or eliminated in seismic data sets by the use of Fourier filters and, to a lesser degree, nonlinear statistical filters. Although these methods are quite useful under specific conditions, they may produce undesirable effects for the low signal to noise ratio data. In this paper, a new method, multi-scale ridgelet transform, is used in the light of the theory of ridgelet transform. We employ wavelet transform to do sub-band decomposition for the signals and then use non-linear thresholding in ridgelet domain for every block. In other words, it is based on the idea of partition, at sufficiently fine scale, a curving singularity looks straight, and so ridgelet transform can work well in such cases. Applications on both synthetic data and actual seismic data from Sichuan basin, South China, show that the new method eliminates the noise portion of the signal more efficiently and retains a greater amount of geologic data than other methods, the quality and consecutiveness of seismic event are improved obviously as well as the quality of section is improved.

A new image watermarking framework based on levels-directions decomposition in contourlet representation

With the development of digital information technologies,robust watermarking framework is taken into real consideration as a challenging issue in the area of image processing,due to the large applicabilities and its utilities in a number of academic and real environments.There are a wide range of solutions to provide image watermarking frameworks,while each one of them is attempted to address an efficient and applicable idea.In reality,the traditional techniques do not have sufficient merit to realize an accurate application.Due to the fact that the main idea behind the approach is organized based on contourlet representation,the only state-of-the-art materials that are investigated along with an integration of the aforementioned contourlet representation in line with watermarking framework are concentrated to be able to propose the novel and skilled technique.In a word,the main process of the proposed robust watermarking framework is organized to deal with both new embedding and de-embedding processes in the area of contourlet transform to generate watermarked image and the corresponding extracted logo image with high accuracy.In fact,the motivation of the approach is that the suggested complexity can be of novelty,which consists of the contourlet representation,the embedding and the corresponding de-embedding modules and the performance monitoring including an analysis of the watermarked image as well as the extracted logo image.There is also a scrambling module that is working in association with levels-directions decomposition in contourlet embedding mechanism,while a decision maker system is designed to deal with the appropriate number of sub-bands to be embedded in the presence of a series of simulated attacks.The required performance is tangibly considered through an integration of the peak signal-to-noise ratio and the structural similarity indices that are related to watermarked image.And the bit error rate and the normal correlation are considered that are related to the extracted logo analysis,as well.Subsequently,the outcomes are fully analyzed to be competitive with respect to the potential techniques in the image colour models including hue or tint in terms of their shade,saturation or amount of gray and their brightness via value or luminance and also hue,saturation and intensity representations,as long as the performance of the whole of channels are concentrated to be presented.The performance monitoring outcomes indicate that the proposed framework is of significance to be verified.

Multi-scale prediction of MEMS gyroscope random drift based on EMD-SVR

To improve the prediction accuracy of micro-electromechanical systems(MEMS)gyroscope random drift series,a multi-scale prediction model based on empirical mode decomposition(EMD)and support vector regression(SVR)is proposed.Firstly,EMD is employed to decompose the raw drift series into a finite number of intrinsic mode functions(IMFs)with the frequency descending successively.Secondly,according to the time-frequency characteristic of each IMF,the corresponding SVR prediction model is established based on phase space reconstruction.Finally,the prediction results are obtained by adding up the prediction results of all IMFs with equal weight.The experimental results demonstrate the validity of the proposed model in random drift prediction of MEMS gyroscope.Compared with a single SVR model,the proposed model has higher prediction precision,which can provide the basis for drift error compensation of MEMS gyroscope.

基于AVMD和排列熵的t分布邻域嵌入流形HHO-SVM模拟电路故障诊断方法

随着信息大数据时代的到来,对于电子系统的依赖程度越来越高,因此模拟电路的故障诊断的准确度要求与日俱增。而模拟电路故障诊断困难,是电子系统诊断维修的瓶颈。本文提出基于自适应变分模态分解(AVMD)和排列熵(PE)的t分布邻域嵌入流形哈里斯鹰优化支持向量机(HHO-SVM)模拟电路故障诊断方法。首先,利用AVMD对待测电路的观测信号进行自适应变分模态分解,得到多组IMF信号,不仅可以克服噪声干扰,而且可以来自适应地确定分解模式的数量,进一步提升分解精度;再对IMF计算排列熵,以充分体现IMF不同时段局部特征,二者相结合构建故障特征向量。并在此基础上,采用t分布式随机邻域嵌入(t-SNE)实现特征空间的流形学习和降维,构建具有良好区分度且保留原来的局部结构特征的故障特征向量;最后依靠哈里斯鹰优化支持向量机(HHO-SVM),使其具有良好的分类准确度,从而最终完成电路故障诊断。通过仿真验证,结果显示,本文方法故障诊断正确率可达100%,效果良好。

基于注意力特征解耦的跨年龄身份成员推理

生成对抗网络(GANs)模型可以生成高分辨率的“不存在”的物体真实图像,近期被广泛应用于各种人工合成数据,尤其是人脸图像生成领域。然而,由于基于该模型的人脸生成器通常需要根据不同身份高度敏感的面部图像进行训练,其中存在潜在数据泄露使得攻击者能够对身份成员关系进行推断的问题。为此,首先设计对查询身份所获取样本与其实际参与训练样本之间存在巨大差异时的身份成员推理攻击,这些差异会导致基于样本推理身份成员关系的性能急剧下降;其次,在此基础上设计基于各身份解耦表征的重建误差攻击方案,在最大化消除不同样本间背景姿势等因素影响的同时,消除巨大年龄跨度导致的表征差异,进一步提高了攻击性能;最后,基于3个代表性的人脸数据集在3个主流GAN架构上训练生成模型并进行攻击,实验结果表明,在各种攻击场景下,此攻击方案较对比方法AUCROC值平均提高0.2。

Three-Dimensional Density Distribution and Seismic Activity along the Guxiang–Tongmai Segment of the Jiali Fault,Tibet

The Guxiang-Tongmai segment of the Jiali fault is situated northeast of the Namche Barwa Syntaxis in northeastern Tibet.It is one of the most active strike-slip faults near the syntaxis and plays a pivotal role in the examination of seismic activity within the eastern Himalayan Syntaxis.New study in the research region has yielded a 1:200000 gravity dataset covering an area 1500 km^(2).Using wavelet transform multiscale decomposition,scratch analysis techniques,and 3D gravity inversion methods,gravity anomalies,fault distributions,and density structures were determined across various scales.Through the integration of our new gravity data with other geophysical and geological information,our findings demonstrate substantial variations in the overall crustal density within the region,with the fault distribution closely linked to these density fluctuations.Disparities in stratigraphic density are important causes of variations in the capacity of geological formations to endure regional tectonic stress.Earthquakes are predominantly concentrated within the density transition zone and are primarily situated in regions of elevated density.The hanging wall stress within the Guxiang-Tongmai segment of the Jiali fault exhibits a notable concentration,marked by pronounced anisotropy,and is positioned within the density differential zone,which is prone to earthquakes.

三维薛定谔方程组的线性Profile分解

为了研究线性薛定谔方程组解Strichartz估计的紧性缺失问题,针对H•^(1)(R^(3))×H•^(1)(R^(3))中的三维线性薛定谔方程组的有界解向量序列,使用解序列的Profile分解方法,构造为解向量子列的(1/√h_(n))U(t-t_(n))/h_(n)^(2),(x-x_(n))/h_(n)类型的Profile分解和.其中,U是线性薛定谔方程组的解向量,在Strichartz范数估计下具有一个很小的余项.首先确定伸缩变换参数序列族,利用傅里叶变换和迭代的思想确定Profile分解族.其次验证了Profile分解和的收敛性,说明了Strichartz范数下余项的收敛性.最后证明了当线性薛定谔方程组的解序列有界时,都可以分解为解向量子列和的形式.

Rolling bearing performance degradation evaluation by VMD and embedding selection-based NPE

In order to improve the incipient fault sensitivity and stability of degradation index in the rolling bearing performance degradation evaluation process,an embedding selection-based neighborhood preserving embedding(ESNPE)method is proposed.Firstly,the acquired vibration signals are decomposed by variational mode decomposition(VMD),and the singular value and relative energy of each intrinsic mode function(IMF)are extracted to form a high-dimensional feature set.Then,the NPE manifold learning method is used to extract the embedded features in the feature space.Considering the problem that useful embedding information is easily suppressed in NPE,an embedding selection strategy is built based on the Spearman correlation coefficient.The effectiveness of embeddings is measured by the coefficient absolute value,and useful embeddings are preserved in the early stage of bearing degradation by using the first-order difference method.Finally,the degradation index is established using the support vector data description(SVDD)model and bearing performance degradation evaluation is achieved.The proposed method was tested with the whole life experiment data of a rolling bearing,and the result was compared with the feature extraction methods of traditional principal component analysis(PCA)and NPE.The results show that the proposed method is superior in improving the incipient fault sensitivity and stability of the degradation index.

KERNEL NEIGHBORHOOD PRESERVING EMBEDDING FOR CLASSIFICATION

The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction method.However, it is confined to linear transforms in the data space.For this, based on the NPE algorithm, a new nonlinear dimensionality reduction method is proposed, which can preserve the local structures of the data in the feature space.First, combined with the Mercer kernel, the solution to the weight matrix in the feature space is gotten and then the corresponding eigenvalue problem of the Kernel NPE(KNPE) method is deduced.Finally, the KNPE algorithm is resolved through a transformed optimization problem and QR decomposition.The experimental results on three real-world data sets show that the new method is better than NPE, Kernel PCA(KPCA) and Kernel LDA(KLDA) in performance.

Interpretations of gravity and Songliao Basin with Wavelet magnetic anomalies in the Multi-scale Decomposition

In this paper, we introduce the method of Wavelet Multi-scale Decomposition （WMD） combined with Power Spectrum Analysis （PSA） for the separation of regional gravity and magnetic anomalies. The Songliao Basin is situated between the Siberian Plate and the North China Plate, and its main structural trend of gravity and magnetic anomaly fields is NNE. The study area shows a significant feature of deep collage-type construction. According to the feature of gravity field, the region was divided into five sub-regions. The gravity and magnetic fields of the Songliao Basin were separated using WMD with a 4th order separation. The apparent depth of anomalies in each order was determined by Logarithmic PSA. Then, the shallow high-frequency anomalies were removed and the 2nd-4th order wavelet detail anomalies were used to study the basin＇s major faults. Twenty-six faults within the basement were recognized. The 4th order wavelet approximate anomalies were used for the inversion of the Moho discontinuity and the Curie isothermal surface.

基于改进CEEMDAN和t-SNE的故障特征提取方法

针对非线性、非稳定振动信号难以提取有效故障特征的问题,提出一种基于改进自适应噪声完备集合经验模态分解(CEEMDAN)和t-分布随机邻域嵌入(t-SNE)算法相结合的故障特征提取方法。利用三次Hermite插值代替三次样条插值构造包络线,提高传统CEEMDAN对非平稳信号的分解精度;利用改进后的CEEMDAN对原始信号分解并通过相关系数筛选出有效固有模态分量(IMF),提取有效IMF分量的时频特征、奇异值和能量值构建高维混合域特征集;最后,通过t-SNE算法挖掘高维混合域特征信息得到低维敏感特征,并将其输入到支持向量机中进行分类,以分类准确率作为特征提取效果评价指标。在齿轮箱故障模拟实验台进行实验验证,结果表明该方法能够准确地提取故障特征,为故障特征提取提供新思路。

重要特征选择和局部网络拓扑嵌入的社区发现算法

寻找网络中连接紧密的、稳定的社区,对网络大数据的挖掘和分析具有重要的意义和价值.节点属性和网络拓扑对社区发现都有重要的影响,由于真实网络中的节点属性维度大,找寻重要属性困难,而且和深层次的结构信息又不易进行高效整合以进行社区划分.为了有效地提取节点的重要属性信息,并和局部链接拓扑信息深入融合,根据矩阵分解,提出了基于特征选择和属性网络嵌入的社区发现算法.首先采用节点的联合相似度潜在表征指导特征选择,筛选出重要的属性后与原拓扑组成新网络,然后将新网络通过融合邻居信息的属性网络表征学习映射成节点低维向量,最后对该嵌入向量进行聚类从而实现社区划分.在真实网络数据集上与其他代表性算法进行比较,实验结果表明所提算法具有良好的特征选择性能和社团划分性能.

基于含权k-壳分解的分组教学虚拟网络映射算法

提出一种两阶段的基于含权k-壳分解的分组教学虚拟网络映射算法。该算法根据含权k-壳分解法对底层网络进行预处理,然后沿着节点间的最短路径映射链路,并结合分组教学优化模型的分组、教学、自学与互学的优化策略,实现节点和链路的协调映射,从而进一步提高解的质量。仿真结果表明,所提算法作为一种多目标的虚拟网络映射算法,能够有效减少链路开启量,提升虚拟网络请求接受率及长期收益成本比。

知识图谱的增强CP分解链接预测方法

CP分解作为知识图谱链接预测的方法之一,能够对一些包含常规数据的知识图谱进行链接预测补全。但当知识图谱存在大量稀疏数据及可逆关系时,该方法不能体现两个实体间具有的隐藏联系,无法对此类数据进行处理。为解决上述问题,提出增强CP分解方法,对三元组中前实体和后实体的两个嵌入向量分别进行学习,并在训练过程中使用概率方法生成更高质量的负例三元组,引入ELU损失函数和AMSGrad优化器,有效对可逆关系和稀疏数据进行处理。在通用数据集上的实验结果表明,所提方法可以有效提升链接预测精度,与对比模型相比取得了5%的性能提升,同时应用在汽车维修知识图谱数据集补全中,取得83.2%正确率的实体补全结果。

多尺度特征提取与非线性融合的综合能源系统多元负荷短期预测

为提高综合能源系统多元负荷短期预测的精度,提出一种基于多尺度特征提取与非线性融合的综合能源系统多元负荷短期预测方法。首先,采用皮尔逊相关系数对气象数据进行关联因子优选;然后,通过嵌入式分解模块将输入的时间序列分解为周期分量和趋势分量,并将分解后得到的输入矩阵并行送入到具有不同尺度卷积核的时间卷积网络中,进行多尺度特征提取;接着,将多尺度时间卷积网络输出的特征向量输入到各自对应的注意力机制,以进行全局信息的学习与融合;最后,采用自适应非线性融合模块对各注意力机制的输出进行非线性融合,得到最终多元负荷预测结果。实验结果表明,所提方法具有较好的预测性能及泛化性。

基于FSMO的SVM训练核的设计与实现

为了解决支持向量机(Support Vector Machine,SVM)训练的复杂性与实时性,本文提出基于单循环的快速序列最小优化算法(Fast Sequential Minimal Optimization,FSMO)来构建新的SVM训练模型.首先,针对传统序列最小优化算法(Sequential Minimal Optimization,SMO)中待优化乘子选择繁复问题,提出了轮询加随机的优选方法并设计了单循环迭代的FSMO训练架构,降低算法复杂度.其次,采用集中计算体系结构分模块设计了新的SVM训练IP核.并且将该SVM训练IP核移植到FPGA平台上进行了验证与分析.结果表明,相较于传统SMO的训练IP核,在训练准确率相似的情况下,基于FSMO的SVM训练IP核训练速度提升约39%,可节省约47%的硬件资源.