基于稀疏分类算法的矿物传送设备故障诊断方法

针对现有基于特征频率识别的矿物传送设备故障诊断方法存在易受强噪声干扰的问题,提出了基于稀疏分类算法的矿物传送设备故障诊断方法。首先,利用计算机测取设备已知故障类型的振动信号,并对其进行傅里叶变换;然后,以傅里叶变换系数构造训练字典,将待测故障类型的振动信号傅里叶变换系数在该训练字典上进行稀疏分解,求取稀疏系数;最后,利用重构信号最小误差判别故障类型。仿真和测试结果表明,该方法能有效诊断出矿物传送设备中轴承的故障类型,为煤矿传送设备的故障监测提供了一种新方法。     

基于分类稀疏表示的人脸表情识别

为挖掘不同人脸表情图像的统计特性差异,提出一种基于分类稀疏表示的表情识别算法。首先通过对不同类别表情图像的字典学习,构建满足各类表情图像统计特性的基函数子集,进而采用Lasso算法获得表情图像在由基函数集所张成特征子空间中的稀疏表示,最后通过比较表情图像在各基函数子集上的重构误差实现不同表情的分类识别。基于JAFFE人脸表情数据库的实验结果表明,该算法可以有效克服人脸身份对表情识别的影响,具有较高的表情识别率和鲁棒性。     

快速移不变稀疏分类算法在线识别汽油机故障

针对移不变稀疏编码算法在线运行时效率不高的问题,提出一种能够明显提高移不变稀疏编码效率的快速算法,并结合稀疏分类实现对汽油发动机故障的在线识别。该算法首先把移不变问题从时域转换到频域上,然后采用特征标记法和拉格朗日对偶法对稀疏系数和分类字典进行求解,在保证稀疏识别精度的同时大幅降低了问题求解的时间复杂度,从而有效改善了发动机故障在线识别系统的实时性。在发动机上的实验结果表明,该算法在怠速和1?500~2?000?r/min工况下对五种常见机械故障的平均识别精度分别为92.35%和91.44%,和其他识别算法大致持平。但其平均在线分类时间仅为13.91?ms和14.5?ms,且分类字典的平均训练速度同样仅为1.43?s和1.47?s,均明显快于其他识别算法。     

基于Gabor特征和支持向量引导字典学习的人脸识别

稀疏编码中的字典学习在稀疏表示的图像识别中扮演着重要的作用。由于Gabor特征对表情、光照和姿态等变化具有一定的鲁棒性,提出一种基于Gabor特征和支持向量引导字典学习（GSVGDL）的稀疏表示人脸识别算法。先提取图像的Gabor特征,然后用增广Gabor特征矩阵来构造初始字典。字典学习模型中综合了重构误差项、判别项和正则化项,判别项公式化定义为所有编码向量对平方距离的加权总和;通过字典学习同时得到字典原子与类别标签相对应的结构化字典和线性分类器。该字典学习方法能够自适应地为不同的编码向量对分配不同的权值,提高了字典的判别性能。实验结果表明该方法具有很好的识别精度和较高的识别效率。     

基于稀疏编码和禁忌优化的故障信号抽取方法

为了克服经典正交匹配算法获取原子集时遍历冗余字典具有较大时间开销的缺点,提出了一种基于压缩感知理论和禁忌优化算法的的稀疏故障信号特征提取方法;首先引入了压缩感知模型并描述了基于信号稀疏表示的故障诊断原理,设计了满足RIP准则以最小化l1范数为目标的稀疏信号解的求解方法,然后定义了一种基于正交匹配算法的稀疏信号重构算法,并以最小化余量为目标函数,采用改进的禁忌搜索算法在原子空间中搜索满足目标函数的最优原子集,最后,给出了基于稀疏编码和禁忌优化混合模型的故障信号提取算法;在Matlab仿真环境下对滚动轴承故障信号进行试验,仿真结果表明:文章方法能有效地对具有强噪声的故障信号进行稀疏重构,不仅具有较高的信噪比,而且具有较小的余量误差和仿真时间,与其它方法相比,具有较大的优越性。     

基于改进MOD学习的滚动轴承振动信号稀疏表示

基于过完备字典的振动信号稀疏表示是滚动轴承信号研究的新热点。提出一种改进MOD字典学习的算法,并用于滚动轴承振动信号的稀疏表示。该方法基于MOD（Method of Optimal Direction）训练学习过程,通过构造分段重叠训练矩阵,能够得到更为稀疏的变换系数。相对DCT、FFT和未改进的处理方法,该方法得到的变换系数更稀疏。将该方法应用到基于压缩感知的滚动轴承振动信号处理,在相同的重构误差范围内,该方法所需要的观测数更少,计算量更小。     

基于CS理论的24脉波整流器开路故障诊断方法

针对24脉波整流器晶闸管开路故障待处理数据量大、诊断精度不高和诊断速度慢的缺点,提出一种基于压缩感知（CS）理论对开路故障电压信号的稀疏向量进行特征提取的分类识别方法。利用冗余字典和高斯测量矩阵对原始信号进行稀疏表示和测量,接着用正则化自适应匹配追踪算法对测量信号进行重构,得到稀疏向量;对稀疏向量进行6种特征参数的提取,将其作为BP神经网络的输入,实现对开路故障的诊断识别;选取典型的开路故障类型,进行仿真实验验证。仿真结果表明,传统方法要处理的数据长度为1000,而所提方法要处理的数据长度只有50,以很少的数据量保存了原有信号的特征信息,使得开路故障识别准确率显著提高,诊断速度加快。     

基于动态字典学习的欠定盲语音重构算法

为提高传统字典学习方法选用固定的语音分段长度重构源信号的精度,提出基于动态字典学习的欠定盲语音重构算法,以提取信号中最优的稀疏表示特征。在欠定语音盲分离的两步法框架下,利用正则化Sim CO字典学习对信号进行稀疏表示,依据最速下降思想通过改变语音分段长度迭代优化信号的重构结果直至收敛,得到信号恢复的总体最优解。实验结果表明,相较传统算法,动态Sim CO字典学习算法进一步提取了信号在字典稀疏域的语音特征,在保证运行成本低的同时有效提高了欠定盲语音的重构质量。     

基于自适应稀疏字典学习的图像盲分离技术研究

稀疏性对多通道观测的信号源分离具有重要作用,现有的算法只能有效的分离稀疏域已知的信号源.为了解决这个问题,可以将字典学习与信号源分离相结合.定义一个代价函数,采用ELad等人提出的降噪方法使其最小化.由于直接采用降噪方法使代价函数最小化难于实现,可以采用一种分级的字典学习方法,根据每一个信号源建立一个自适应的局部字典.该方法能够在噪声条件下有效的提高信号源分离的效果,仿真结果表明了该方法的有效性.     

压缩感知中块结构字典学习方法改进

基于稀疏聚集的块结构字典学习方法不能对字典原子支撑集数目差别大的情况进行辨别的问题,提出了一种利用球面K-均值学习块结构字典的方法,将字典原子支撑集差别纳入考虑范围,通过余弦距离判别将相近的字典原子聚类,形成具有非均匀块结构的字典;利用学习得到的块结构字典对信号进行重构.仿真实验表明:与离散余弦基(DCT)、无结构字典和基于稀疏聚集的块结构字典相比,改进方法学习的字典与图像信号的匹配度更好,有效地提高了图像重构质量,降低了信号的重构误差.     

Dictionary learning with the cosparse analysis model based on summation of blocked determinants as the sparseness measure

Dictionary learning is crucially important for sparse representation of signals. Most existing methods are based on the so called synthesis model, in which the dictionary is column redundant. This paper addresses the dictionary learning and sparse representation with the so-called analysis model. In this model, the analysis dictionary multiplying the signal can lead to a sparse outcome. Though it has been studied in the literature, there is still not an investigation in the context of dictionary learning for nonnegative signal representation, while the algorithms designed for general signal are found not sufficient when applied to the nonnegative signals. In this paper, for a more efficient dictionary learning, we propose a novel cost function that is termed as the summation of blocked determinants measure of sparseness (SBDMS). Based on this measure, a new analysis sparse model is derived, and an iterative sparseness maximization scheme is proposed to solve this model. In the scheme, the analysis sparse representation problem can be cast into row-to-row optimizations with respect to the analysis dictionary, and then the quadratic programming (QP) technique is used to optimize each row. Therefore, we present an algorithm for the dictionary learning and sparse representation for nonnegative signals. Numerical experiments on recovery of analysis dictionary show the effectiveness of the proposed method.     

An AK-BRP dictionary learning algorithm for video frame sparse representation in compressed sensing

Sparsifying transform is an important prerequisite in compressed sensing. And it is practically significant to research the fast and efficient signal sparse representation methods. In this paper, we propose an adaptive K-BRP (AK-BRP) dictionary learning algorithm. The bilateral random projection (BRP), a method of low rank approximation, is used to update the dictionary atoms. Furthermore, in the sparse coding stage, an adaptive sparsity constraint is utilized to obtain sparse representation coefficient and helps to improve the efficiency of the dictionary update stage further. Finally, for video frame sparse representation, our adaptive dictionary learning algorithm achieves better performance than K-SVD dictionary learning algorithm in terms of computation cost. And our method produces smaller reconstruction error as well.

     

基于K-SVD超声渡越时间获取方法研究

在超声回波检测信号中,反映污垢特征的冲击信号非常微弱,容易被噪声淹没。针对信号稀疏分解中常用匹配追踪分解不够准确的问题,提出基于K-SVD奇异值分解的超声渡越时间获取方法,利用K-SVD训练得到超声回波信号的过完备字典,结合正交匹配追踪进行局部搜索适配原子,以提高信号稀疏分解的速度和准确度。基于Comsol Multipysics仿真软件建立充液污垢管道三维有限元模型,研究了超声回波传播特性规律。将K-SVD算法应用于超声回波仿真信号和换热污垢管道回波检测信号的处理,并与原始小波训练字典进行对比。结果表明：改进的K-SVD字典学习算法能够在提高信号稀疏分解的同时,获得较好的降噪结果和污垢特征信息提取,对超声检测信号的处理具有实际意义。     

字典学习模型、算法及其应用研究进展

稀疏表示模型常利用训练样本学习过完备字典, 旨在获得信号的冗余稀疏表示. 设计简单、高效、通用性强的字典学习算法是目前的主要研究方向之一, 也是信息领域的研究热点. 基于综合稀疏模型的字典学习方法已经广泛应用于图像分类、图像去噪、图像超分辨率和压缩成像等领域. 近些年来, 解析稀疏模型、盲字典模型和信息复杂度模型等新模型的出现丰富了字典学习理论, 使得更广泛类型的信号能够被简单性描述. 本文详细介绍了综合字典、解析字典、盲字典和基于信息复杂度字典学习的基本模型及其算法, 阐述了字典学习的典型应用, 指出了字典学习的进一步研究方向.     

AKSNet: A novel convolutional neural network with adaptive kernel width and sparse regularization for machinery fault diagnosis

Convolutional kernels have significant affections on feature learning of convolutional neural network (CNN). However, it is still a challenging problem to determine appropriate kernel width. Moreover, some features learned by convolutional layers are still redundant and noisy. Thus, adaptive selection of kernel width and feature selection of feature maps are key techniques to improve feature learning performance of CNNs. In this paper, a new deep neural network (DNN) model, adaptive kernel sparse network (AKSNet) is proposed to extract multi-scale fault features from one-dimensional (1-D) vibration signals. Firstly, an adaptive kernel selection method is developed, where multiple branches with different kernels are used to extract multi-scale features from vibration signals. Channel-wise attention is developed to fuse features generated by these kernels to obtain different informative scales. Secondly, a spatial attention is used for dynamic receptive field to focus on salient region of feature maps. Thirdly, a sparse regularization layer is embedded in the deep network to further filter noise and highlight impaction of the feature maps. Finally, two cases are adopted to verify effectiveness of AKSNet-based feature learning for bearing fault diagnosis. Experimental results show that AKSNet can effectively extract features from multi-channel vibration signals and then improves fault diagnosis performance of the classifier significantly. AKSNet shows better recognition performance in comparison with that of shallow neural networks and other typical DNNs.     

Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory

Extracting reliable features from vibration signals is a key problem in machinery fault recognition. This study proposes a novel sparse wavelet reconstruction residual (SWRR) feature for rolling element bearing diagnosis based on wavelet packet transform (WPT) and sparse representation theory. WPT has obtained huge success in machine fault diagnosis, which demonstrates its potential for extracting discriminative features. Sparse representation is an increasingly popular algorithm in signal processing and can find concise, high-level representations of signals that well matches the structure of analyzed data by using a learned dictionary. If sparse coding is conducted with a discriminative dictionary for different type signals, the pattern laying in each class will drive the generation of a unique residual. Inspired by this, sparse representation is introduced to help the feature extraction from WPT-based results in a novel manner: (1) learn a dictionary for each fault-related WPT subband; (2) solve the coefficients of each subband for different classes using the learned dictionaries and (3) calculate the reconstruction residual to form the SWRR feature. The effectiveness and advantages of the SWRR feature are confirmed by the practical fault pattern recognition of two bearing cases.     

随钻测量用MEMS陀螺信号稀疏提取

针对随钻测量用MEMS陀螺检测信号特性,提出采用稀疏表示的方法进行信号提取.首先从检测的陀螺调制信号构成角度,分析其信号稀疏特性;然后分析检测信号特性,构造与之最相似的过完备词典;比较已有稀疏重构算法优劣性,提出一种改进的稀疏度自适应匹配追踪算法对陀螺调制信号进行稀疏提取,进而解调真实陀螺信号;最后采用提出的改进SAMP算法于新构造的过完备字典中进行陀螺信号稀疏提取实验,并与小波阈值提取法进行实验对比,实验结果表明:采用新构造的字典和改进的SAMP算法,可以有效提取MEMS陀螺真实信号,提取效果优于传统小波阈值法.     

基于稀疏字典的听觉显著性计算

听觉注意显著性计算模型是研究听觉注意模型的基本问题,显著性计算中选择合适的特征是关键,本文从特征选择的角度提出了一种基于稀疏字典学习的听觉显著性计算模型.该模型首先通过K-SVD字典学习算法学习各种声学信号的特征,然后对字典集进行归类整合,以选取的特征字典为基础,采用OMP算法对信号进行稀疏表示,并直接将稀疏系数按帧合并得到声学信号的听觉显著图.仿真结果表明该听觉显著性计算模型在特征选择上更符合声学信号的自然属性,基于基础特征字典的显著图可以突出噪声中具有结构特征的声信号,基于特定信号特征字典的显著图可以实现对特定声信号的选择性关注.