基于二维主元分析的间歇过程故障诊断

传统的多向主元分析(MPCA)已广泛应用于监视多变量间歇过程。在MPCA算法中,三维的间歇过程数据需要转换为高维的二维向量,导致计算量和存储空间大,同时不可避免地丢失一些重要信息。因此,提出一种新的基于二维主元分析(2DPCA)的故障诊断方法。由于每个批次的间歇过程数据是一个二维向量(矩阵),应用以各个批次矩阵为分析对象的2DPCA算法,避免矢量化,存储空间和存储需求小;另外,2DPCA采用各个批次的协方差的平均值来进行建模,能够更加准确地反映出不同类型的故障,在一定程度上增强了故障诊断的准确性。半导体工业实例的监视结果说明,2DPCA方法优于MPCA。     

基于ISTA-LSTM模型的间歇过程质量预测

为了考虑过程变量与质量变量的相关性,解决间歇过程的时序性和动态特性导致预测精度不高的问题,提出一种基于改进时空注意力-长短时记忆神经网络(improved spatial and temporal attention long short-term memory,ISTA-LSTM)的模型进行间歇过程质量预测.首先,对间歇过程的三维数据按变量方向展开成二维矩阵,对二维数据采用Min-max方法归一化;然后,使用偏最小二乘(PLS)方法对原始数据降维,提取数据的特征信息,基于时间注意力和空间注意力构建编码-解码器结构的双层LSTM网络,利用注意力机制自适应地学习神经网络参数,关注每一个过程变量对质量变量的重要性并分配相应的注意值,从而保留所有输入的必要信息,采用带交叉验证的网格搜索算法对预测模型进行超参数寻优,并建立预测模型;最后,在青霉素发酵仿真平台上进行实验验证,实验结果验证了所提模型对间歇过程质量预测的可行性和有效性.     

青霉素发酵过程的2DPCA建模及监测

针对多向主元分析（multi-way principal component analysis,MPCA）算法用于间歇过程实施监控时需要将三维数据转换为高阶的二维矩阵,从而易导致算法的计算量大,且会丢失一些有用信息的情况进行了研究,提出了一种新的间歇过程故障诊断方法——二维主成分分析法（2-dimensional principal component analysis,2DPCA）。该算法首先利用各个批次的二维矩阵构造协方差矩阵,进而求得所有批次协方差矩阵的平均值进行建模,大大降低了计算复杂度,运算时间较MPCA缩短了19/20到3/4,且无须占用太多存储空间;同时,2DPCA计算协方差矩阵较MPCA更为准确,取协方差矩阵的平均值能够更加精确地反映不同类型的故障,在一定程度上增强了故障诊断的准确率。最后,通过将所提出的方法应用于青霉素发酵过程的监控中,验证了该算法的有效性和准确性。     

基于NMF-SVM的复杂化工过程故障诊断

针对复杂化工过程中存在故障数据维数大与识别率低的问题,提出了1种非负矩阵分解与支持向量机相结合的故障诊断方法。该方法首先对原始特征数据进行非负矩阵分解,得到基向量矩阵与系数矩阵,用基向量矩阵作为输入训练SVM分类器,针对NMF结果的不稳定性,采用PCA模型确定NMF的初始值;然后通过系数矩阵构造超定线性方程组,并将其最小二乘解作为样本特征矩阵输入分类器进行故障类型的识别。通过对Tennessee Eastman(TE)过程数据的仿真研究,实验结果验证了该方法的有效性。     

基于中心化相似度矩阵的词向量方法

词向量使用低维稠密向量表示词，通过向量运算能够反映词间关系，被广泛应用于自然语言处理任务。对基于矩阵分解的词向量方法进行了研究，发现降维前相似度矩阵质量与词向量质量存在线性相关性，提出了一种基于中心化相似度矩阵的方法。该方法使得相似（不相似或弱相似）词间的相似程度相对增强（减弱）。在WS-353和RW数据集的词语相似性实验中验证了所提出方法的有效性，两个数据集下词向量质量最高提升0.2896和0.1801。中心化能够提升降维前相似度矩阵质量，进而提升词向量质量。     

基于MFA-ICA的间歇过程在线监控

针对间歇过程独特的数据特点,提出1种将因子分析(FA)作为独立成分分析(ICA)白化预处理手段的多向因子分析白化独立成分分析(multiway factor analysis-independent component analysis,MFA-ICA)间歇过程监控方法.因子分析充分考虑了模型误差的普遍意义,拥有优秀的噪声建模能力.将其代替上成分分析用于白化,可以更好的提取数据集的本质信息.首先将间歇过程三维数据依次按批次和变量展开得到二维数据矩阵,接着把上述方法用于展开后的数据,利用ICA的,I2统计图实现在线故障检测.该方法用于标准仿真平台Pensim,结果表明上述方法对于提高间歇过程故障检测的快速性,降低漏报率有明显效果.     

动态融合的多视图投影聚类算法

多视图聚类是一个日益受到关注的研究热点。现有的大多数多视图聚类方法通常先对数据进行图学习,再对融合得到的统一图进行聚类得到最终结果,这种图学习和图聚类的两步策略可能导致聚类结果具有随机性。此外,多视图数据本身存在不可避免的噪声并且各视图数据差异较大,在原始高维数据空间进行无效融合可能造成重要信息的损失,不同多视图数据也可能存在选择参数敏感的问题。为了解决上述问题,提出了一种动态融合的多视图投影聚类算法,将自适应降维图学习、无参数的自权重图融合和谱聚类整合在同一框架中,三个过程相互促进,联合优化投影矩阵、相似性矩阵、共识矩阵以及聚类标签。对动态融合过程中得到的共识矩阵的拉普拉斯矩阵施加秩约束,直接获得聚类结果。而且引入的启发式超参数会随着每次优化迭代自动调整。为了求解联合优化问题,设计了一种有效的交替迭代方法。在人工数据集和真实数据集上得到的实验结果表明该算法的优越性。     

基于传递熵的MPCA间歇过程监测方法

传统统计分析方法忽略了变量间作用关系,而传递熵可以有效地表达变量间作用关系,因此提出了一种基于传递熵的MPCA间歇过程监测方法.利用传递熵表达变量间的作用关系,在计算传递熵时采用非参数核密度估计法,利用该方法不依赖于数据先验分布知识的特点来处理非高斯分布的过程数据,通过构建传递熵矩阵,结合滑动窗,实现对间歇过程变量间信息传递的动态表达,最后对传递熵矩阵进行多向主元分析方法(MPCA)建模,实现间歇过程监测.通过青霉素发酵的仿真,结果表明与传统多变量统计过程控制(MSPC)方法作对比,本文监测方法能更及时准确地监测到过程异常.     

基于PARAFAC2时段划分的间歇过程故障检测

间歇过程的多时段特性直接影响多元统计分析过程建模的准确性。针对间歇过程多时段特性,本文提出一种基于平行因子分解2（PARAFAC2）时段划分的间歇过程故障检测方法,首先对每一个时间片矩阵进行PARAFAC2建模,得到时间片矩阵的模型控制限,然后从间歇过程初始时刻开始,按照时序依次将每个时间片添加到时间块并进行PARAFAC2建模,得到时间块矩阵的模型控制限,通过评估时间片和时间块模型控制限的差异性确定初始时段划分点,并利用时段评价划分指标（PPCI）获取最佳的时段划分结果,最后在所得结果基础上分别对各个时段构建MPCA故障检测模型,实现间歇过程故障检测。所提方法保留了间歇过程三维结构特征和数据的完整性,深入考虑了间歇过程实际运行的时序性,提高了故障检测的准确性。利用青霉素发酵过程仿真实验验证了所提方法的有效性。     

作用于块奇偶性的二值图像数据隐藏算法研究

提出一种作用于奇偶性矩阵之上的二值图像数据隐藏算法.算法首先对原始图像进行分块,获取原图的奇偶性矩阵和标志矩阵;再对这些矩阵进行二次分决,使用标志矩阵衡量块的可嵌入性,选取可嵌入的奇偶性矩阵块并在其上修改至多一个值来嵌入数据.嵌入过程中,从要修改的奇偶性矩阵值对应的原始图像块中翻转可修改程度最高的像素,使得奇偶性矩阵块所代表的信息和嵌入信息相一致.文中对算法隐藏容量、数据嵌入后图像质量和安全性进行了分析.实验结果表明,该算法有图像质量好、修改像素少、嵌入容量大等优点,并能很好地应用于二值图像的认证中.     

基于GMM的间歇过程故障检测

对间歇过程的多操作阶段进行划分时,往往会被离群点和噪声干扰,影响建模的精确性,针对此问题提出一种新的方法:主元分析--多方向高斯混合模型(Principal component analysis-multiple Gaussian mixture model, PCA-MGMM)建模方法.首先用最短长度法对数据进行等长处理,融合不同展开方法相结合的处理方式消除数据预估问题;利用主元分析方法将数据转换到对故障较为敏感的低维子空间中,得到主元的同时消除了离群点和噪声的干扰;通过改进的高斯混合模型(Gaussian mixture model, GMM)算法对各阶段主元进行聚类,减少了运算量的同时自动得到最佳高斯成分和对应的统计分布参数;最后将局部指标融合为全局概率监控指标,实现了连续的在线监控.通过一个实际的半导体制造过程的仿真研究验证了所提方法的有效性.     

Robust blind source separation by beta divergence

Blind source separation is aimed at recovering original independent signals when their linear mixtures are observed. Various methods for estimating a recovering matrix have been proposed and applied to data in many fields, such as biological signal processing, communication engineering, and financial market data analysis. One problem these methods have is that they are often too sensitive to outliers, and the existence of a few outliers might change the estimate drastically. In this article, we propose a robust method of blind source separation based on the beta divergence. Shift parameters are explicitly included in our model instead of the conventional way which assumes that original signals have zero mean. The estimator gives smaller weights to possible outliers so that their influence on the estimate is weakened. Simulation results show that the proposed estimator significantly improves the performance over the existing methods when outliers exist; it keeps equal performance otherwise.     

Fault detection based on robust characteristic dimensionality reduction

In this paper, a novel fault detection method is developed based on robust characteristic dimensionality reduction (RCDR). The time-constrained sparse representation (TCSR) method is firstly introduced by considering the space and time characteristics of industrial process monitoring data simultaneously. It can remove space-related outliers, time-related outliers and noises by solving an optimization problem. Then, a new RCDR method is proposed, which fully utilizes the constructed robust adjacency graph and considers the data characteristics. Its scatter matrices are specially designed by consideration of the data characteristics of fault detection. The within-class scatter matrix only characterizes normal data set with a classic covariance matrix, while the inter-class scatter matrix characterizes the separability between normal data and fault data through a pre-defined scatter matrix. It is worth mentioning that our method does not make Gaussian assumptions about the distribution of the fault data, and the number of projection directions is not limited as well. The TCSR is also embedded into our proposed dimensionality reduction method, enabling it to handle the fault detection problem under strong disturbances. Simulations on Tennessee Eastman process (TEP) and a case study of electric multiple unit (EMU) braking system of high-speed trains fully demonstrate the effectiveness and applicability of our proposed fault detection method.     

Batch process monitoring with tensor factorization

A batch process monitoring method using tensor factorization, tensor locality preserving projections (TLPP), is proposed. In many existing vector-based methods on batch process monitoring such as MPCA and MLPP, a batch data is represented as a vector in high-dimensional space. But vectorizing batch data will lead to information loss. Essentially, a batch data is presented as a second order tensor, or a matrix. In this case, tensor factorization may be used to deal with the two-way batch data matrix directly instead of performing vectorizing procedure. Furthermore, tensor representation has some advantages such as low memory and storage requirements and less estimated parameters for normal operating condition (NOC) model. On the other hand, different from principal component analysis (PCA) which aims at preserving the global Euclidean structure of the data, the TLPP aims to preserve the local neighborhood information and to detect the intrinsic manifold structure of the data. Consequently, TLPP may be used to find more meaningful intrinsic information hidden in the observations. The effectiveness and advantages of the TLPP monitoring approach are tested with the data from a benchmark fed-batch penicillin fermentation and two industrial fermentation processes, penicillin and cephalosporin, respectively.     

鲁棒PLS在间歇生产过程监控中的应用

间歇和半间歇过程在化学工业中占有重要地位,如何对其进行监控一直是过程控制领域研究的热点之一.现实过程中,数据大都存在离群点,易使多向部分最小二乘(MPLS)模型造成误差.针对MPLS统计监控受离群点影响的问题,提出一种基于鲁棒MPLS的统计监控分析和相应鲁棒监控统计量的计算方法.相对于普通MPLS,鲁棒MPLS在建模数据中存在离群点时仍能给出正确的统计监控模型,降低了建模过程对数据的要求.     

A novel support vector regression for data set with outliers

Support vector machine (SVM) is sensitive to the outliers, which reduces its generalization ability. This paper presents a novel support vector regression (SVR) together with fuzzification theory, inconsistency matrix and neighbors match operator to address this critical issue. Fuzzification method is exploited to assign similarities on the input space and on the output response to each pair of training samples respectively. The inconsistency matrix is used to calculate the weights of input variables, followed by searching outliers through a novel neighborhood matching algorithm and then eliminating them. Finally, the processed data is sent to the original SVR, and the prediction results are acquired. A simulation example and three real-world applications demonstrate the proposed method for data set with outliers.     

Two-way imputation: A Bayesian method for estimating missing scores in tests and questionnaires, and an accurate approximation

Previous research has shown that method two-way with error for multiple imputation in test and questionnaire data produces small bias in statistical analyses. This method is based on a two-way ANOVA model of persons by items but it is improper from a Bayesian point of view. Proper two-way imputations are generated using data augmentation. Simulation results show that the resulting method two-way with data augmentation produces unbiased results in Cronbach's alpha, the mean of squares in ANOVA, the item means, and small bias in the mean test score and the factor loadings from principal components analysis. The data with imputed scores result in statistics having a slightly larger standard deviation than the original complete data. Method two-way with error produces results that are only slightly more biased, especially for low percentages of missingness. Thus, it may serve as an accurate approximation to the more involved method two-way with data augmentation.     

一种潜在信息约束的非负矩阵分解方法

传统的非负矩阵分解方法没有充分利用数据间的内在相似性,从而影响了算法的性能。为此,本文提出一种潜在信息约束的非负矩阵分解方法。该方法首先利用迭代最近邻方法挖掘原始数据的潜在信息,然后利用潜在信息构造数据之间的相似图,最后将相似图作为约束项求得非负矩阵的最优分解。相似图的约束使得非负矩阵分解在降维过程中保持了原始数据之间的相似性关系,进而提高了非负矩阵分解的判别能力。图像聚类实验结果表明了该方法的有效性。