Monitoring of quality-relevant and quality-irrelevant blocks with characteristic-similar variables based on self-organizing map and kernel approaches

Variables in quality-related process monitoring can be divided into quality-relevant and quality-irrelevant groups depending on the correlation with the quality indicator. These variables can also be separated into multiple sets in which variables are closely relevant to one another because of the interdependence of the process. Block monitoring with reasonable variable partition and reliable model can distinguish quality-related and quality-unrelated faults and improve monitoring performance. A block monitoring method based on self-organizing map (SOM) and kernel approaches is proposed. After collecting and normalizing the sample data including process variables and quality ones, the data matrix is transposed. The inverted samples are used as the input of SOM, and variables with the same behavioral characteristic and a close correlation are topologically mapped in a similar area. Accordingly, samples can be visually blocked into quality-relevant and independent subspaces. Given the nonlinearity of industrial process, kernel partial least squares (KPLS) and kernel principal component analysis (KPCA) are employed to monitor the two types of blocks. The information provided by fault detection can reveal the effects on quality indicators and the location of faults. Finally, the effectiveness of SOM-KPLS/KPCA is evaluated using a numerical example and the Tennessee–Eastman process.     

Parallel quality-related dynamic principal component regression method for chemical process monitoring

Traditional quality-related process monitoring mainly focuses on the magnitude change of the quality variables caused by additive faults. However, the abnormal fluctuations in the quality variables caused by multiplicative faults are often overlooked. In this paper, a novel parallel dynamic principal component regression (P-DPCR) algorithm is proposed to monitor the changes in the magnitude and fluctuation of the quality variables simultaneously. Firstly, in order to eliminate the interference of quality-unrelated variables, the quality-related process variables are selected on the basis of correlation analysis. Secondly, the dynamic extension and moving window are carried out for process variables and quality variables, in which the dynamic variables space (called X-space/Y-space) and the variance space (called VX-space/VY-space) are constructed. Afterwards, double quality-related statistics based on the regression model of these four spaces are given, and the comprehensive monitoring decision can be obtained. Finally, two numerical cases and the Tennessee Eastman process are used to show the effectiveness of the proposed method.     

基于自适应混合核典型变量分析的工业过程质量相关故障检测

质量相关故障检测技术是保障工业过程安全顺行和质量稳定的重要手段,是当前流程工业过程控制领域的研究热点.针对工业过程的非线性与动态特性及其质量相关故障的时变特性,提出一种基于自适应混合核典型变量分析(AMKCVA)的质量相关故障检测方法.该方法通过设计合理的混合核函数和自适应监测统计量,提升了工业过程质量相关故障的检测性...     

Information concentrated variational auto-encoder for quality-related nonlinear process monitoring

As the deep learning technology develops, many process monitoring methods based on auto-encoder (AE) are designed for the nonlinear industrial processes. However, these methods mainly focus on process variables and ignore the quality indicator which is crucial for the final production. To extract the latent variables which represent both process information and quality information, this paper proposes a novel algorithm named information concentrated variational auto-encoder (IFCVAE). To concentrate the quality-related information, a loading matrix regularization based on mutual information is designed, so that the strongly quality-related variables tend to have larger weights in the loading matrix. In addition, to monitor processes from the quality-related and unrelated aspects, IFCVAE decomposes the original space into two subspaces that are mutually orthogonal based on variational auto-encoder (VAE). With the help of an additional regression network, the two subspaces can correspond to the quality-related and unrelated spaces. For process monitoring, two statistics are designed for the subspaces according to Kullback–Leibler divergence. Finally, the effectiveness of IFCVAE is demonstrated by a numerical case and an industrial case.     

自动编码器与典型相关分析方法联合驱动的工业过程质量监测

本文将自动编码器(AE)特征提取方法和典型相关分析方法(CCA)有机结合,提出了一种联合驱动的质量监测模型及其质量相关的故障检测方法.首先,利用AE算法对输入样本进行无监督自动学习和重构,实现数据的特征提取和降维;其次,利用CCA算法实现特征与质量变量关联最大化,建立质量变量与特征变量的关系模型;根据监测模型的潜结构投影,构建T2统计量和SPE统计量及其相应控制限.将提出的方法用于分析带钢热连轧过程现场实际数据,结果表明,基于自动编码器-典型相关分析方法(AE-CCA)的质量监测方法能够准确的检测出故障,并且检测效果优于传统的核典型相关分析(KCCA)算法.     

基于KVAE-OCCA的质量相关故障检测方法及应用

为了解决非线性过程质量相关故障检测问题,提出了一种名为关键变量自编码器-正交典型相关分析(KVAE-OCCA)的方法.首先,为了挑选出与质量变量具有相关性的过程变量,计算过程变量和质量变量的互信息,选择具有较大互信息的过程变量.然后,利用自编码器对选择出的过程变量进行无监督学习,实现特征提取和降维.其次,利用正交典型相...     

Supervised linear dynamic system model for quality related fault detection in dynamic processes

Dynamic and uncertainty are two main features of industrial processes data which should be paid attentions when carrying out process monitoring and fault diagnosis. As a typical dynamic Bayesian network model, linear dynamic system (LDS) can efficiently deal with both dynamic and uncertain features of the process data. However, the quality information has been ignored by the LDS model, which could serve as a supervised term for information extraction and fault detection. In this paper, a supervised form of the LDS model is developed, which can successfully incorporate the information of quality variables. With this additional data information, the new supervised LDS model can provide a quality related fault detection scheme for dynamic processes. A detailed industrial case study on the Tennessee Eastman benchmark process is carried out for performance evaluation of the developed method.     

基于高效核偏最小二乘的质量相关故障检测

核偏最小二乘(KPLS)是一种多元统计方法, 广泛应用于过程监控, 然而, KPLS采用斜交分解, 导致质量相关空间存在冗余信息易引发误报警. 因此, 本文提出了高效核偏最小二乘(EKPLS)模型, 所提方法通过奇异值分解(SVD)将核矩阵正交分解为质量相关空间和质量无关空间, 有效降低质量相关空间中的冗余信息, 并采用主成分分析(PCA)按方差大小将质量相关空间分解为质量主空间和质量次空间. 此外, 为进一步降低由质量无关故障引发的误报警, 提出基于质量估计的正交信号修正(OSC)预处理方法, 并结合EKPLS模型提出了OSC-EKPLS算法. OSCEKPLS通过质量估计值对被测数据进行OSC预处理, 降低了计算复杂度和误报率. 最后, 通过数值仿真和田纳西–伊斯曼过程验证了OSC-EKPLS具有良好的故障检测性和更低的误报率.     

基于局部信息增量与MPLS的质量相关故障检测方法

在工业生产中,对系统进行故障检测具有十分重要的作用.改进的偏最小二乘(modified partial least squares,MPLS)是在PLS基础上提出的一种扩展算法,在质量相关故障检测中具有良好的检测效果,但当测试数据中含有质量无关故障时,MPLS算法漏报率较高.另外,MPLS算法的阈值为固定值会导致其误报率增加,这些问题会对工业过程监控产生较大影响.鉴于此,提出一种基于局部信息增量与MPLS的质量相关故障检测方法(local information increment-MPLS,LII-MPLS).在MPLS基础上,通过使用局部信息增量技术对测试数据进行实时更新检测后,质量相关故障的漏报率明显降低.同时,过程复杂化导致静态控制限不能满足故障检测的需求,现存的动态控制限适用范围具有一定局限性,因此改进静态控制限将其推广为局部动态阈值.最后,通过田纳西伊士曼过程(Tennessee Eastman process,TEP)仿真实验验证了所提出算法的有效性.     

基于核典型相关性-熵成分分析的工业过程质量监测方法

工业过程多变量、数据高维度和非线性的特点使得对其质量监测及质量相关的故障诊断变得复杂.融合核熵成分分析(KECA)及典型相关分析(CCA)方法的思想,进行特征提取降维的同时确保所提取特征与质量变量的最大相关性,提出一种新的质量相关的工业过程故障检测方法.首先,采用KECA对输入数据进行核空间的映射及特征提取,同时融合CCA算法思想使得所提取特征与质量变量间关联最大化;然后,构建监测统计量并用Parzen窗估计其控制限,用于过程的故障检测;最后,运用所提方法对带钢热连轧工业过程实际生产数据进行分析,并与其他4种传统非线性算法对比分析,实验结果验证了所提方法的准确性、有效性及先进性.     

加权k最近邻重构分析的工业过程故障诊断

k--最近邻(k--nearest neighbor, k--NN)是一种有效的基于数据驱动的故障检测方法, 该方法在工业过程监视方面已经得到了广泛的应用. 但在过程中存在故障时, 精确地寻找故障根源和识别故障变量是故障诊断的重要目标, 也是保证工业过程安全生产的重要任务. 本文在k--NN故障检测技术的基础上, 提出了一种加权的k--NN重构方法, 对使控制指标减小最大(maximize reduce index, MRI)的过程变量依次进行重构, 进而确定发生故障的传感器. 根据理论分析并结合数值仿真对提出的方法进行了验证, 数值仿真先从精度方面验证了该方法能够有效地对故障传感器数值进行重构, 然后验证了该方法不仅适用于单一传感器 故障诊断, 对于同时发生或者因变量相关性而传播的传感器故障也具有很好的效果. 最后, 该方法被成功应用于TE(Tennessee Eastman)化工过程.     

基于偏最小二乘的质量相关多模态故障检测技术

偏最小二乘(PLS)算法通常适用于稳定工况下的工业过程故障检测.在日趋复杂的工业过程中,过程数据通常不满足正态分布,存在非线性、动态、多模态等问题.针对多模态问题,已有大量模态区分方法可用,但这些方法都未考虑质量相关因素,因此并不适用于质量相关类算法.为此,针对质量相关类算法提出新的质量相关模态区分规则,该规则通过核模糊聚类对添加线性递增时间变量的数据在时间方向上进行初步的聚类,再通过质量相关指标进一步准确划分模态;同时,过程复杂化导致静态控制限不能满足故障检测的需求,现存的动态控制限适用范围具有一定的局限性,可通过改进动态控制限将其推广为广义动态综合控制限.实验中,先是基于两种非线性偏最小二乘模型将新方法应用于青霉素发酵过程故障检测中,极大减少了漏报率和误报率.最后,通过数值仿真实验验证了添加线性递增时间变量的合理性.     

Contribution rate plot for nonlinear quality-related fault diagnosis with application to the hot strip mill process

In this paper, a nonlinear fault diagnosis scheme is established for the hot strip mill process (HSMP). In HSMP, the faults affecting quality index are denoted as quality-related faults, which should be taken care as soon as possible. Projection to latent structures (PLS) is a basic model for quality-related fault detection in linear processes. In the presented work, a total kernel PLS (T-KPLS) model is utilized for modeling and monitoring HSMP, which is a typical nonlinear process. However, diagnosis tools have not been developed aiming at the nonlinear case based on T-KPLS model. Motivated by the successful use of contribution plot for the linear case, a contribution rate plot is proposed to extend contribution plots to the nonlinear case. In the end of this paper, the proposed method is applied to the hot strip mill process effectively.     

基于公共特有子空间提取的工业设备多模式运行过程故障检测方法

工业设备运行状态直接影响到最终产品质量,有必要对设备运行过程开展监控,因此着重对工业设备运行数据中存在的不同阶次信息以及多模式等复杂数据特性展开讨论.针对过程中存在的不同阶次信息问题,首先通过引入最大交互熵展开与偏最小二乘方法,将原始空间信息分解为高阶和低阶信息,并构建相应隐空间模型来提取高阶与低阶质量相关关系;其次,...     

Process monitoring and quality variable prediction utilizing PLS in industrial fed-batch cell culture

Partial least squares is a data-driven modeling technique that has been utilized for process monitoring in a variety of industrial processes. This paper develops a novel online partial least squares approach (evolving PLS) and compares it with an existing online PLS technique (global PLS). Both methods are applied to an industrial fed-batch mammalian cell culture process, where process variables are used to predict a key quality variable, product titer. Fault detection and diagnosis are performed using PLS models and statistical metrics. This new detection approach was able to recognize a variety of faults during online monitoring.     

基于核直接分解的过热蒸汽系统故障诊断方法

为提高过热蒸汽系统的运行效率并减少非紧要故障的报警率,本文提出一种质量相关的非线性故障检测与诊断方法.首先,利用核函数将过程变量映射到高维特征空间以消除原始变量之间的非线性耦合.然后,在特征空间进行核直接分解得到两个正交子空间,并在两个子空间中分别设计统计量指标进行质量相关的故障检测.在此基础上,利用偏微分贡献图提取每个变量对联合统计量指标的贡献率,并根据贡献率大小最终确定故障变量.仿真结果表明,所提出的方法能够准确区分影响过热蒸汽温度和不影响过热蒸汽温度的故障,有效降低了非紧要故障的报警率,提高了过热蒸汽系统的运行效率.     

Quality relevant nonlinear batch process performance monitoring using a kernel based multiway non-Gaussian latent subspace projection approach

Multiway kernel partial least squares method (MKPLS) has recently been developed for monitoring the operational performance of nonlinear batch or semi-batch processes. It has strong capability to handle batch trajectories and nonlinear process dynamics, which cannot be effectively dealt with by traditional multiway partial least squares (MPLS) technique. However, MKPLS method may not be effective in capturing significant non-Gaussian features of batch processes because only the second-order statistics instead of higher-order statistics are taken into account in the underlying model. On the other hand, multiway kernel independent component analysis (MKICA) has been proposed for nonlinear batch process monitoring and fault detection. Different from MKPLS, MKICA can extract not only nonlinear but also non-Gaussian features through maximizing the higher-order statistic of negentropy instead of second-order statistic of covariance within the high-dimensional kernel space. Nevertheless, MKICA based process monitoring approaches may not be well suited in many batch processes because only process measurement variables are utilized while quality variables are not considered in the multivariate models. In this paper, a novel multiway kernel based quality relevant non-Gaussian latent subspace projection (MKQNGLSP) approach is proposed in order to monitor the operational performance of batch processes with nonlinear and non-Gaussian dynamics by combining measurement and quality variables. First, both process measurement and quality variables are projected onto high-dimensional nonlinear kernel feature spaces, respectively. Then, the multidimensional latent directions within kernel feature subspaces corresponding to measurement and quality variables are concurrently searched for so that the maximized mutual information between the measurement and quality spaces is obtained. The I² and SPE monitoring indices within the extracted latent subspaces are further defined to capture batch process faults resulting in abnormal product quality. The proposed MKQNGLSP method is applied to a fed-batch penicillin fermentation process and the operational performance monitoring results demonstrate the superiority of the developed method as apposed to the MKPLS based process monitoring approach.     

基于递归稀疏主成分分析的工业过程在线故障监测和诊断

提出一种基于递归稀疏主成分分析(recursive sparse principal component analysis,RSPCA)的工业过程故障监测与诊断方法,可用于时变工业过程的自适应故障监测与诊断.通过引入弹性回归网,将主成分问题转化为Lasso与Ridge结合的凸优化问题,采用秩-1矩阵修正对协方差矩阵进行递归分解,递归更新稀疏载荷矩阵和监测统计量的过程控制限,以实现连续工业过程长时间自适应故障监测,对检测出来的故障通过贡献图法实现对故障的诊断.在田纳西-伊斯曼(TE)过程进行实验验证,结果表明,与传统的故障监测方法相比,所提出的方法有效降低了故障漏检率和误报率,且时间复杂度低,确保了故障监测的灵敏度和实时性.     

偏最小二乘线性模型及其非线性动态扩展模型综述

偏最小二乘(Partial least square,PLS)是一种基于数据驱动可以处理多个因变量对多个自变量的回归建模方法,因其具有提取质量相关信息的特性,在质量相关复杂工业过程监控中得到广泛的应用,成为近几十年复杂工业过程故障检测和诊断领域的研究热点.对此,介绍线性、非线性、动态PLS模型及其故障检测技术.首先,介绍标准PLS模型,在此基础上对传统PLS模型进行细化分并指出其优缺点,针对标准PLS存在的两个问题以及工业过程数据的两种极端情况,从数据预处理类、多空间类和分块类三方面梳理线性PLS模型的发展和改进历程;其次,将非线性PLS模型扩展方法分为两类,重点介绍核函数非线性PLS模型的研究现状;再次,指出动态扩展方法的两种基本思路,对PLS动态模型进行分类,阐明动态特性的成因,从本质上揭示两种动态扩展方法的原理,按照分类综述动态PLS模型的发展现状;最后,指出该领域亟需解决的问题和未来研究方向.     

基于Bagging-CVA的动态过程及质量相关故障检测

针对过程数据具有时序相关性以及过程故障是否影响产品质量的问题,提出一种基于Bagging思想和典型变量分析(CVA)的故障检测方法(Bagging-CVA).采用Bagging思想对建模数据随机抽样构成多组新的数据集,消除数据的时序相关性.分别在每组新的数据集基于CVA方法建立过程相关和质量相关的故障检测模型,同时监测...