二维非负矩阵分解在齿轮故障诊断中的应用

针对齿轮故障信号时频分布识别问题,提出采用二维非负矩阵分解技术提取时频分布矩阵特征参数的方法。采用S变换技术将齿轮故障信号变换至时频域,为克服传统的一维非负矩阵分解对矩阵向量化带来的维数过高和结构信息损失问题,提出采用二维非负矩阵分解技术直接对信号时频分布矩阵提取特征参数。对齿轮5种状态下信号时频分布矩阵的特征提取和分类结果表明,二维非负矩阵分解技术无论在计算效率还是分类精度上都明显优于一维非负矩阵分解技术。     

采用改进投影梯度非负矩阵分解和非采样Contourlet变换的图像融合方法

针对非负矩阵分解(NMF)算法时间复杂度较高,而投影梯度(PG)优化方法可以大幅降低NMF约束优化迭代问题的时间复杂度,提出一种基于改进的投影梯度NMF(IPGNMF)和非采样Contourlet变换(NSCT)相结合的图像融合方法.采用NSCT对已配准的源图像进行多尺度、多方向的分解,将分解后的低频部分作为原始数据,...     

基于非负矩阵分解的产品结构相似性判断及其应用

为了快速发现可重用产品结构,提出了基于非负矩阵分解的产品结构相似性判断方法。通过将产品结构邻接矩阵转化为邻接向量,构建包含全部结构信息的库矩阵;利用Multiplicative Updates(MU)算法对库矩阵进行非负矩阵分解,实现以低维空间向量描述的产品结构;在此基础上,通过计算低维向量的欧氏距离,可以判断产品结构之间的相似性;最后通过实例对所提出原理和方法进行了验证,结果表明,该方法比目前的相似性判断方法更高效。     

基于β散度约束的非负矩阵分解的机械复合故障诊断方法

时频分析经常被用来刻画非平稳振动信号的局部信息,而经时频变换后的特征信号具有较高的矩阵维数,很难对高维特征矩阵直接进行分类或特征提取.为此,提出了基于时频分析与β散度约束的非负矩阵分解算法(NMF)相结合的机械复合故障诊断方法.对采集的振动信号进行时频分析,获取局部特征信息;利用β-NMF算法实现数据的降维,并根据特征信息重构信号;在β-NMF算法中引入加权脉冲因子(CIF),对重构后的信号进行筛选;将得到的分离信号进行包络频谱分析,实现故障诊断.以滚动轴承复合故障为研究对象进行验证,分析结果表明:所提出的方法可以有效提取出外圈与滚动体冲击性特征,实现了滚动轴承的复合故障诊断.     

基于WNMF和区域分维的图像融合算法

针对非负矩阵分解图像融合算法细节表现能力不足的缺陷,提出了一种基于加权非负矩阵分解和区域分维相结合的红外与可见光图像融合算法。在研究图像区域分维性质的基础上,用不同尺度上的区域分维来获取加权系数。通过设计加权系数的获取方法,重点突出边缘像素和小区域,以提高加权非负矩阵分解图像融合算法的细节提取能力,并得到最符合人眼视觉效果的融合图像。与现有基于标准或各种改进非负矩阵分解图像融合算法的对比实验表明,所提算法在平均梯度等表示细节信息的指标上提高了19%以上,有效改善了标准非负矩阵分解图像融合算法存在的不足。     

几何约束等价性及等价迭代研究

针对几何约束系统中不可构造的柔性复合顶点的求解问题,引入等价约束,分裂部分约束以打破柔性复合顶点的强耦合性,形成只包含简单顶点的求解序列。基于等价约束的性质和结构拓扑,研究了求解序列顶点存在与不存在实数解情况下,雅可比矩阵的求解方法,从而以少量分裂约束方程组对等价约束变量的等价迭代,或者柔性复合顶点的局部迭代,取代柔性复合顶点的整体数值迭代求解,降低迭代的维数,算法实现于几何约束求解引擎CBA(constraint broadcast automa-tion)中。     

基于稀疏性非负矩阵分解的滚动轴承复合故障诊断

将时频图像非负矩阵分解算法加以改进,提出时频图像稀疏性非负矩阵分解(SNMF)算法,并将其用于滚动轴承复合故障诊断.滚动轴承复合故障信号的双谱时频图中蕴含了丰富的故障信息,对双谱进行稀疏性非负矩阵分解分析,提取时频图的稀疏系数矩阵,将其分别作为训练及测试特征向量输入到支持向量数据描述(SVDD)算法,进而实现滚动轴承的3类复合故障类型(内圈外圈复合故障、滚动体外圈复合故障及外圈内圈滚动体复合故障)的正确分类.     

基于非负矩阵分解算法的工程陶瓷磨削表面损伤检测

考虑表面损伤检测在工程陶瓷表面质量评价中的重要作用,首次把非负矩阵分解(NMF)图像重构算法引入工程陶瓷磨削表面损伤检测中,并进行了理论分析与实例检测。首先,将输入图像数据集从原始数据空间降维到一个低维NMF空间,利用本文提出的图像重构相对误差0.1监督规则,确定子空间基r值。然后,利用两个低维非负矩阵进行图像重构,获取磨削纹理背景图像,并通过图像减法去除磨削纹理。最后,利用Canny边缘检测算法提取工程陶瓷磨削表面损伤图像。实验结果表明,该方法能够准确提取表面损伤并计算磨削损伤率评价参数。     

柴油机振动谱图纹理增强的NMF特征提取方法

针对柴油机振动加速度信号特征提取困难的问题,提出一种对时频图像纹理信息进行增强的矩阵非负分解方法,用于提取柴油机振动信号的时频特征。首先,利用匹配追踪算法(MP)结合Wigner-Ville分布获取时频分辨率较好的时频表征;然后引入局部二值模式(LBP)算子对时频图像的灰度矩阵重新编码,再利用非负矩阵分解算法(NMF)获取时频图像对应的低维特征参量,以增强时频图像的纹理特征,提高NMF的特征提取效果。通过对柴油机4种不同状态的振动信号进行分析研究,可得出结论:该方法能有效地表达柴油机缸盖振动加速度信号的时频特征,可用于准确地诊断柴油机的气门故障。     

基于非负矩阵分解的单通道故障特征分离方法

针对单通道振动信号的多特征分离问题,提出了一种基于正交非负矩阵分解的故障特征提取方法。首先,采用短时傅里叶变换,利用时频分布来描述信号中的局部故障特征,通过核心一致性指标评估子空间维数;然后,在幅值谱矩阵分解的基础上,通过正交性约束实现低维嵌入分量信息的分离,获取局部特征的准确描述;最后,采用相位恢复理论重构出特征波形,对仿真信号和滚动轴承故障数据进行了测试。结果表明,所提出的方法能利用单通道信号有效地分离出微弱的局部故障特征,为机械状态的早期故障诊断识别提供了一种有效手段。     

Feature Extraction and Recognition for Rolling Element Bearing Fault Utilizing Short-Time Fourier Transform and Non-negative Matrix Factorization

Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, the time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classify the high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution(TFD) with non-negative matrix factorization(NMF), and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform(STFT). Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network(ANN), NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.     

正交迭代局部Fisher判别转子故障诊断

通过局部加权邻接矩阵重新定义类内散度和类间散度,建立局部Fisher判别函数,在特征值求解过程中以正交迭代方式找出最优投影向量,得到故障诊断模型。该方法能保证数据降维过程中的重构误差最小,并可直接运用故障诊断模型识别增量数据,避免了一般流形学习模式识别时对动态增量数据需要重建模型的问题。转子故障诊断试验表明,对于多传感器振动特征融合信号,相对其他流形学习算法,正交局部Fisher判别(orthogonl locally Fisher discriminant,简称OLFD)的故障诊断效果最好。     

神经网络结构与算法对变压器故障诊断的影响

BP神经网络算法本质上是基于梯度下降的一种迭代学习算法,存在学习收敛速度慢、收敛精度低、易陷入局部极小、学习率难以选取、隐层数及隐层神经元个数难以确定等缺陷。为了选择出更适宜变压器DGA故障诊断的神经网络结构及算法。本文采用了常用的几种智能算法对变压器故障样本进行了诊断性能对比实验。结果得出Levenberg-Marquardt神经网络算法是收敛速度较快的算法,有动量和自适应的梯度下降法是收敛稳定性较佳的算法;网络最优结构设计过程。为用于变压器DGA故障诊断的神经网络的结构和算法提供了系统化的试验方法。     

柴油机振动信号快速稀疏分解与二维特征编码

针对柴油机故障诊断方法中的信号时频表征及特征提取问题,提出一种基于振动信号快速稀疏分解与二维时频特征编码识别的柴油机智能故障诊断方法。首先,为了获得时、频聚集性优良的时频图像,提出一种随分解残差信号自适应更新Gabor字典的改进匹配追踪(adaptive matching pursuit,简称AMP)算法,利用AMP算法将柴油机振动信号分解后叠加各原子分量的Wigner-Ville分布,获取原信号的稀疏分解时频图像;然后,为提取时频图像的特征参量,提出了双向二维非负矩阵分解(two-directional,2-dimensional non-negative matrix factorization,简称TD2DNMF)算法,用于对时频图像的幅值矩阵进行特征编码,获取蕴含在时频图像内部的低维特征,并利用最近邻分类器实现了时频图像的自动分类识别。将提出的方法应用于4种不同状态柴油机气门故障的诊断试验中,结果表明,该方法能够获得无交叉项干扰、聚集性好的时频图像,使各时频分量的物理意义更加明确,并改进了传统图像模式识别中的特征参数提取方法,是一种有效的柴油机故障诊断方法。     

An improved LLE algorithm based on iterative shrinkage for machinery fault diagnosis

Local linear embedding (LLE) algorithm is widely utilized to feature extraction for fault diagnosis, but the diagnosis result is sensitive to reconstruction weight W of LLE. To make W more significant and robust, in this paper, ISLLE algorithm is proposed with the aid of iterative shrinkage technology and LLE algorithm. In ISLLE algorithm, a surrogate function is introduced, upon which the high-dimensional optimization problem can be decoupled into a set of one-dimensional equations, then W can be easily computed by iterative shrinkage method. In each iteration, the small and negative weight coefficients are eliminated, while the large ones are shrunk, which can be regarded as feature extraction and noise reduction. Hence, the signals processed by ISLLE are more beneficial to diagnosis. Three real datasets are used to examine the proposed method. The experimental results demonstrate that the proposed method is valid, and the performance of ISLLE outperforms that of the original LLE.     

基于IPSO-PNN的电动汽车故障诊断

为了提高电动汽车故障诊断的准确性,提出了一种基于改进粒子群(Improved Particle Swarm Optimization,IPSO)算法优化概率神经网络(Probabilistic Neural Network,PNN)的电动汽车故障诊断方法,即基于IPSO-PNN的电动汽车故障诊断方法.首先,研究了基于P...     

Feature selection for machine fault diagnosis using clustering of non-negation matrix factorization

Feature selection has been attracting more attentions in recent years for its advantages in improving the fault diagnosis efficiency and reducing the cost of feature acquisition. In this paper, we regard the feature selection as a clustering process with data decomposition technique and propose a novel feature selection method based on the non-negation matrix factorization (NMF). Alternating Least Squares (ALS) algorithm with sparsity control and decorrelation constrains is adopted to factorize original feature space into two low-rank matrixes (projection vectors and feature spaces). Considering the clustering distribution of the projection space, the optimal feature vectors are calculated by the means of the best updating rule parameters. Besides, the inverse of feature vectors is furtherly utilized in the seeking feature subset, which ensures high classifying performance. Experiments are performed by using two standard data sets and the fault diagnosis of roller bearing case. The results are compared with those obtained by applying the whole feature set and standard feature selection algorithms. The outcomes of comparative analysis have confirmed the effectiveness of the proposed approach.     

周期信号参数高精度估计方法与应用

给出连续谱峰搜索地高精度估计参数的牛顿迭代法格式。针对频谱的起伏剧烈,用基因算法修正了迭代过程。仿真结果表明,连续谱峰搜索法估计参数的远高于传统的ＦＦＴ谱线算法。旋转机械和心音的两个产例都表明连续谱搜峰法的优越性。     

Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings

Rolling element bearings (REBs) play an essential role in modern machinery and their condition monitoring is significant in predictive maintenance. Due to the harsh operating conditions, multi-fault may co-exist in one bearing and vibration signal always exhibits low signal-to-noise ratio (SNR), which causes difficulties in detecting fault. In the previous studies, maximum correlated kurtosis deconvolution (MCKD) has been validated as an efficient method to extract fault feature in the fault signals. Nonetheless, there are still some challenges when MCKD is applied to fault detection owing to the rigorous requirements of multiple input parameters. To overcome limitation, a multi-objective iterative optimization algorithm (MOIOA) for multi-fault diagnosis is proposed. In this method, correlated kurtosis (CK) is taken as a criterion to select optimal Morlet wavelet filter using the whale optimization algorithm (WOA). Meanwhile, to further eliminate the effect of the inaccurate period on CK, the update process of period is incorporated. After that, the simulated and experimental signals are utilized to testify the validity and superiority of the MOIOA for multiple faults detection by the comparison with MCKD. The results indicate that MOIOA is efficient to extract weak fault features even with heavy noise and harmonic interferences.     

Fault diagnosis for diesel valve trains based on non-negative matrix factorization and neural network ensemble

It is well known that the vibration signals are unstable when there is some failure in machinery. So in this paper, the cone-shaped kernel distributions (CKD) of vibration acceleration signals acquired from the cylinder head in eight different states of valve train were calculated and displayed in grey images. Meanwhile, non-negative matrix factorization (NMF) was used to decompose multivariate data, and neural network ensemble (NNE), which is of better generalization capability for classification than a single neural network, was used to perform intelligent diagnosis without further fault feature (such as eigenvalues or symptom parameters) extraction from time–frequency distributions. It is shown by the experimental results that the faults of diesel valve trains can be accurately classified by the proposed method.