基于状态空间主成分分析网络的故障检测方法

作为一种经典的方法,主成分分析(PCA)在多元统计过程监控领域得到了广泛的应用。然而,主成分分析及其各种改进方法仅从原始数据中提取了一层特征,缺乏对深层次特征的提取。计算机领域深度学习技术的发展表明了深层次的网络结构有利于数据特征的提取,因此,将主成分分析网络(PCANet)这种深度学习网络结构引入到故障诊断领域,与多元统计过程监控方法进行结合,以增强故障检测效果。在PCANet框架下,针对工业过程数据的动态特征,在网络结构中增加了状态空间模型作为动态层以解决动态性问题。此外,还以故障检测为目标重新设计了输出层。最后,通过在TE过程上的仿真测试验证了该方法用于故障检测的可行性和有效性。     

基于加权深度支持向量数据描述的工业过程故障检测

传统支持向量数据描述（SVDD）方法本质上采用浅层学习框架,难以有效监控非线性工业过程的复杂故障。针对此问题,提出一种基于加权深度支持向量数据描述（WDSVDD）的故障检测方法。该方法一方面在深度学习框架下重新定义SVDD优化目标函数,构建基于深度特征的深度SVDD监控模型（DSVDD）,并利用核密度估计法计算监控指标的统计控制限;另一方面,考虑到深度特征的故障敏感度差异特性,在DSVDD监控模型中设计特征加权层,分别从静态和动态信息分析角度给出权重因子的计算方法,利用权重因子突出故障敏感特征的影响以提高故障检测率。应用于一个典型化工过程的测试结果表明,所研究的方法能够比传统SVDD方法更有效地监控过程中复杂故障的发生。     

基于DMVU-OCSVM的故障诊断方法

针对工业过程的非线性和动态特性,提出一种基于动态最大方差展开（DMVU）和单类支持向量机（OCSVM）的故障诊断方法DMVU-OCSVM。为了分析数据的动态特性和非线性,应用流形学习技术DMVU提取数据变量中的非线性动态流形特征。基于所提取的流形特征信息建立OCSVM统计模型,并构造非线性监控统计量实时检测过程故障。在连续搅拌反应器（CSTR）系统上的仿真结果说明,本文提出的方法能够比OCSVM更有效地检测过程故障。     

基于加权统计特征KICA的故障检测与诊断方法

针对核独立元分析（kernel independent component analysis, KICA）在非线性动态过程中对微小故障检测率低的问题,提出一种基于加权统计特征KICA（weighted statistical feature KICA, WSFKICA）的故障检测与诊断方法。首先,利用KICA从原始数据中捕获独立元数据和残差数据;然后,通过加权统计特征和滑动窗口获取改进统计特征数据集,并由此数据集构建统计量进行故障检测;最后,利用基于变量贡献图的方法进行过程故障诊断。与传统KICA统计量相比,所提方法的统计量对非线性动态过程中的微小故障具有更高的故障检测性能。应用该方法对一个数值例子和田纳西-伊斯曼（Tennessee-Eastman, TE）过程进行仿真测试,仿真结果显示出所提方法相对于独立元分析（ICA）、KICA、核主成分分析（kernel principal component analysis, KPCA）和统计局部核主成分分析（statistical local kernel principal component analysis, SLKPCA）检测的优势。     

基于改进核主成分分析的故障检测与诊断方法

针对传统基于核主成分分析的故障检测方法提取非线性特征时只考虑全局结构而忽略局部近邻结构保持的问题, 提出基于改进核主成分分析的故障检测与诊断方法。改进核主成分分析方法将流形学习保持局部结构的思想融入核主成分分析的目标函数中, 使得到的特征空间不仅具有原始样本空间的整体结构, 还保持样本空间相似的局部近邻结构, 可以包含更丰富的特征信息。在此基础上, 本文使用改进核主成分分析方法把原始变量空间映射到特征空间, 使用费舍尔判别分析在特征空间中构建距离统计量并通过核密度估计确定其控制限, 进一步利用相似度的性能诊断方法识别发生的故障类型。采用Tennessee Eastman过程故障检测数据集进行的仿真实验表明所提方法可以取得较好的效果。     

基于故障判别增强KECA算法的故障检测

基于核熵主成分分析方法的统计模型仅利用正常工况下数据进行建模，而忽略了监控系统数据库中一些已知类别的先前故障数据。为了利用先前故障数据中包含的故障信息来增强故障检测性能，提出了一种故障判别增强KECA（fault discriminant enhanced kernel entropy component analysis, FDKECA）算法。该法通过采用无监督学习和监督学习方法建立模型，同时监测非线性核熵主成分（kernel entropy component, KEC）和故障判别成分（fault discriminant component, FDC）两类数据特征。此外，利用贝叶斯推理将相应的监视统计信息转换为故障概率，并通过加权两个子模型的结果来构建基于总体概率的监视统计量。通过数值仿真和田纳西伊斯曼（Tennessee Eastman, TE）过程仿真实验，证明和传统KECA相比，FDKECA算法能够有效利用故障数据提高故障检测率。     

基于核可预测元分析的非线性故障检测技术

为克服可预测元分析方法在非线性领域的不足,并更好地反映工业监控过程的动态特性,将核可预测元分析方法引入非线性故障检测领域。首先将观测数据映射到高维特征空间,提取可预测元特征;然后基于贝叶斯定理构造统计量,用于监控工业过程进行并检测故障。在TE模型的仿真实验结果表明:基于核可预测元分析的非线性故障检测方法能有效提高系统的故障检测准确率。     

基于统计信号重构的传感器故障诊断

主元分析、偏最小二层等数据驱动的多元统计监控方法由于不依赖于精确的数学模型,在化工过程监控与故障检测方面取得了广泛应用.通过研究基于统计信号重构的传感器故障诊断算法,给出了统计信号重构算法的一般形式,并推导了基于统计信号重构算法进行传感器故障诊断的可检测与可分离性条件,定义了模型空间和余差空间的故障识别指标.通过CSTR仿真对象的应用比较了不同统计信号重构算法间的差异,验证了故障诊断算法的有效性.     

基于多动态核聚类的间歇过程在线监控

针对传统的多元统计监测方法不能有效检测工业过程中由于初始条件波动较大所引发的弱故障问题,提出一种基于多动态核聚类的核主元分析(DKCPCA)监控策略,实现多阶段间歇过程的弱故障在线监控.该方法首先针对过程中各阶段每一批次数据结合自回归移动平均时间序列模型(ARMAX)和核主成分分析(KPCA)方法分别建立动态核PCA模型,然后根据各批次模型间载荷的相似性采用分层次聚类方法进行聚类,最后将聚在一起的批次数据进行展开重新再建立动态核PCA模型,随着聚类数目的不同从而建立多个类模型.当在线应用时给出了多模型选择策略,以提高监测精度.将此方法应用于青霉素发酵过程的监控中,监测结果表明此方法取得了比DKPCA和MKPCA更好的监测性能.     

基于加权概率CVDA的动态化工系统微小故障检测

典型变量差异度分析（CVDA）是近年来提出的一种新型动态过程监控方法,已在微小故障检测领域获得成功应用。针对传统CVDA方法忽视了特征量的概率信息挖掘问题,提出一种基于加权概率CVDA（WPCVDA）的动态化工系统微小故障检测方法。一方面,该方法在基本CVDA模型特征基础上引入Wasserstein距离（WD）度量特征量概率分布的变化,构造概率化的WD特征提高CVDA模型对微小故障的灵敏度;另一方面,进一步考虑不同的WD特征成分携带故障信息的差异性,设计一种自适应权值计算策略,为关键的故障敏感特征成分设置大的权值,突出其在监控统计量中的作用。在一个标准化工过程的验证结果说明,所提出的WPCVDA方法比传统CVDA方法具有更好的微小故障检测性能。     

基于主元子空间富信息重构的过程监测方法

作为一种经典的多元投影方法,主元分析（PCA）已在多变量统计过程监测领域得到了广泛应用。然而,传统的主元挑选方法往往选择方差较大的主元以表征建模样本中包含的较大信息量,但当过程信息发生变化时,方差较小的主元所表现出来的变异性可能更为明显,即包含的信息量更为丰富,也更有利于故障检出。为此,提出一种基于主元子空间富信息重构的过程监测方法（informative PCA,Info-PCA）。该方法通过计算过程数据在各主元方向上累积T²统计量的变化率,选择变化较为明显的主元以重构主元子空间。在此基础上,建立相应的统计监测模型。最后,通过实例验证该方法用于过程监测的可行性与有效性。     

基于核主角的故障检测方法

基于主元分析（PCA）的统计检测方法已经被广泛应用于各种化工过程的故障检测和识别.移动主元分析（moving principal component analysis,简称MPCA）算法基于PCA,根据主元子空间的变化来判断故障是否发生.然而,基于主元分析的统计检测方法是线性方法,无法有效应用于非线性系统.因此,提出一种适合于非线性系统的故障检测方法——基于核主角(kernel principal angle,简称KPA)的故障检测方法,其基本思想与MPCA相似,主要内容包括构建特征子空间和核主角测量两部分.TE过程故障检测仿真实验证明,基于核主角的故障检测方法优于传统的多元统计检测方法（cMSPC）和MPCA.     

Statistical monitoring of dynamic processes based on dynamic independent component analysis

Most multivariate statistical monitoring methods based on principal component analysis (PCA) assume implicitly that the observations at one time are statistically independent of observations at past time and the latent variables follow a Gaussian distribution. However, in real chemical and biological processes, these assumptions are invalid because of their dynamic and nonlinear characteristics. Therefore, monitoring charts based on conventional PCA tend to show many false alarms and bad detectability. In this paper, a new statistical process monitoring method using dynamic independent component analysis (DICA) is proposed to overcome these disadvantages. ICA is a recently developed technique for revealing hidden factors that underlies sets of measurements followed on a non-Gaussian distribution. Its goal is to decompose a set of multivariate data into a base of statistically independent components without a loss of information. The proposed DICA monitoring method is applying ICA to the augmenting matrix with time-lagged variables. DICA can show more powerful monitoring performance in the case of a dynamic process since it can extract source signals which are independent of the auto- and cross-correlation of variables. It is applied to fault detection in both a simple multivariate dynamic process and the Tennessee Eastman process. The simulation results clearly show that the method effectively detects faults in a multivariate dynamic process.     

基于距离空间统计量分析的多模态过程无监督故障检测

工业过程往往运行于多个生产模态,针对多模态过程数据的空间分布特点,提出了一种新的基于样本距离空间统计量分析的故障检测方法(DSSA)。首先用每一个样本与其训练集样本中的邻居之间的k个最近邻距离之差来表示该样本,将样本从原始变量空间映射到对应的距离空间中。然后在距离空间中通过移动窗口的方式计算各阶统计量,最后对由各阶统计量组成的统计量样本进行主元分析(PCA)。将DSSA方法、PCA方法以及另一种基于k近邻规则的多模态故障检测方法(FD-kNN)应用于TE过程中,仿真结果表明DSSA方法对多模态故障检测更为有效。     

Siamese DeNPE network framework for fault detection of batch process

In batch processes, it is crucial to ensure safe production by fault detection. However, the long batch duration, limited runs, and strong nonlinearity of the data pose challenges. Incipient faults with small amplitudes further complicate the detection process. To achieve safe production, motivated by deep learning strategies, we propose a new fault detection method of batch process called Siamese deep neighbourhood preserving embedding network (SDeNPE). First, the DeNPE network is constructed by means of NPE and kernel functions, which utilizes the different types of kernel functions in the kernel mapping layer to extract diverse deep nonlinear features and overcome strong nonlinearity in the process data. Then, the Siamese network is used to obtain the different features between the data and improve the recognition of incipient faults. In addition, the deep extraction and Siamese network allow for batches of training data reduction without diminishing the performance of fault detection. Finally, we utilize monitoring statistics to complete the fault detection process. Two batch process cases involving the penicillin fermentation process and the semiconductor etching process demonstrate the superior fault detection performance of the proposed SDeNPE over the other comparison methods.     

Reducing smearing effect in contribution plots and improving fault detection via polynomial approximated isomap embeddings

Dimension reduction is an essential method used in multivariate statistical process monitoring for fault detection and diagnosis. Principal component analysis (PCA) and independent component analysis (ICA) are the most frequently used linear dimensional reduction tools, and the contribution plot is the most popular fault isolation method in the absence of any prior information on the faults. These methods, however, come with their shortcomings. The fault detection capability of linear methods may not be sufficient for non-linear processes, and smearing effect is known to deteriorate the diagnostics obtained from contribution plots. While the fault detection rate may be increased by kernelized methods or deep artificial neural network models, tuning data-dependent hyperparameter(s) and network structure with limited historical data is not an easy task. Furthermore, the resulting non-linear models often do not directly possess fault isolation capability. In the current study, we aim to devise a novel method named ICA_pIso-PCA, which offers non-linear fault detection and isolation in a rather straightforward manner. The rationale of ICA_pIso-PCA mainly involves building a non-linear scores matrix, composed of principal component scores and high-order polynomial approximated isomap embeddings, followed by implementation of the ICA-PCA algorithm on this matrix. Applications on a toy dataset and the Tennessee Eastman plant show that the I² index from ICA_pIso-PCA yields a high fault detection rate and offers accurate contribution plots with diminished smearing effects compared to those from traditional monitoring methods. Easy implementation and the potential for future research are further advantages of the proposed method.     

A new multivariate statistical process monitoring method using principal component analysis

Principal component analysis (PCA) has been used successfully as a multivariate statistical process control (MSPC) tool for detecting faults in processes with highly correlated variables. In the present work, a novel statistical process monitoring method is proposed for further improvement of monitoring performance. It is termed ‘moving principal component analysis’ (MPCA) because PCA is applied on-line by moving the time-window. In MPCA, changes in the direction of each principal component or changes in the subspace spanned by several principal components are monitored. In other words, changes in the correlation structure of process variables, instead of changes in the scores of predefined principal components, are monitored by using MPCA. The monitoring performance of the proposed method and that of the conventional MSPC method are compared with application to simulated data obtained from a simple 2×2 process and the Tennessee Eastman process. The results clearly show that the monitoring performance of MPCA is considerably better than that of the conventional MSPC method and that dynamic monitoring is superior to static monitoring.     

Real-time risk monitoring system for chemical plants

This study was performed to develop a Real-Time Risk Monitoring System which helps to do fault detection using the information from plant information systems in a chemical process. In this study, to do fault detection, principal component analysis (PCA) methods of multivariate statistical analysis were used. The fundamental notions are a set of variable combinations, that is, detection of principal components which indicate the tendency of variables and operating data. Besides classical statistic process control, PCA can reduce the dimension of variables with monitoring process. Therefore, they are known as suitable methods to treat enormous data composed of many dimensions. The developed Real-Time Risk Monitoring System can analyze and manage the plant information on-line, diagnose causes of abnormality and so prevent major accidents. It’s useful for operators to treat numerous process faults efficiently.