ISOMAP-LDA方法用于化工过程故障诊断

针对化工连续生产过程的时序性及非线性等特征,提出一种新的基于数据驱动的化工过程故障诊断方法：ISOMAP-LDA。首先实行流形学习算法ISOMAP,在保持量测数据几何结构特性下完成非线性降维,然后基于提取的嵌入变量张成的低维空间,选用线性判别分析（LDA）构造故障模式类的判别函数,负责各采样个体故障类型的判定。将该方法用于仿真化工Tennessee Eastman 过程的故障诊断,结果表明,ISOMAP-LDA方法不仅拥有较高的故障诊断能力,而且取得采样在低维空间的可视化表示。     

基于核Fisher包络分析的间歇过程故障诊断

随着间歇过程越来越受重视,其过程监控和故障诊断技术也成为研究热点。以核Fisher判别分析为基础,提出了基于核Fisher的正常工况与故障包络面模型,给出了基于该模型的在线故障诊断流程。此方法利用了Fisher判别分析对类别的划分特点,分别针对正常工况数据和各故障类型数据建立包络曲面模型。与多向Fisher判别分析相比,该方法按批次方向将数据展开,能够解决生产周期不一致问题,在线故障诊断时也不需要预报完整的生产轨迹,并且加入核函数来处理复杂的非线性。最后在青霉素发酵过程的仿真平台上对该方法进行测试,与改进多向Fisher判别分析方法进行对比,该方法获得了满意的诊断效果：能够及早诊断出故障的发生,并在有效识别已有故障的同时还具有对新故障的自学习能力。     

基于核Fisher包络分析的间歇过程故障诊断

随着间歇过程越来越受重视,其过程监控和故障诊断技术也成为研究热点。以核Fisher判别分析为基础,提出了基于核Fisher的正常工况与故障包络面模型,给出了基于该模型的在线故障诊断流程。此方法利用了Fisher判别分析对类别的划分特点,分别针对正常工况数据和各故障类型数据建立包络曲面模型。与多向Fisher判别分析相比,该方法按批次方向将数据展开,能够解决生产周期不一致问题,在线故障诊断时也不需要预报完整的生产轨迹,并且加入核函数来处理复杂的非线性。最后在青霉素发酵过程的仿真平台上对该方法进行测试,与改进多向Fisher判别分析方法进行对比,该方法获得了满意的诊断效果:能够及早诊断出故障的发生,并在有效识别已有故障的同时还具有对新故障的自学习能力。     

基于KSFDA-SVDD的非线性过程故障检测方法

慢特征分析(SFA)是一种无监督的线性学习算法,没有考虑过程数据的类别信息和非线性特征。针对此问题,提出一种基于核慢特征判别分析(KSFDA)和支持向量数据描述(SVDD)的非线性过程故障检测方法KSFDA-SVDD。该方法首先利用核技巧将数据从原始空间映射到高维空间,然后通过最大化正常工况数据和故障模式数据之间伪时间序列的时间变化同时最小化正常工况数据内部伪时间序列的时间变化计算判别矩阵,最后利用SVDD描述采用判别矩阵降维后的正常工况数据的分布域,构建监控统计量检测过程故障。在连续搅拌反应器(CSTR)过程上的仿真结果表明所提出方法的故障检测性能优于传统的KPCA方法。     

基于RISOMAP的非线性过程故障检测方法

化工过程监控数据存在非线性特点,且过程常常运行于多个模态,针对该类问题,提出基于相对等距离映射(relative isometric mapping, RISOMAP)的过程故障检测方法,该方法采用相对测地距离构造高维空间的距离关系阵,运用多维尺度变换(MDS)计算其低维嵌入输出,从高维数据中提取子流形信息和残差信息分别构造监控统计量进行故障检测,同时运用核ridge回归在线计算测试数据的低维输出,核矩阵通过综合相似度进行更新。数值算例和TE过程的仿真结果表明,RISOMAP方法可以更为有效地实施故障检测,故障检测的灵敏度较高,同时也为基于流形学习的多模态过程故障检测的实施提供了一条思路。     

基于核PLS方法的非线性过程在线监控

针对过程监控数据的非线性特点,提出了一种基于核偏最小二乘（KPLS）的监控方法。KPLS方法是将原始输入数据通过核函数映射到高维特征空间,然后在高维特征空间再进行偏最小二乘（PLS）运算。与线性PLS相比,KPLS方法能充分利用样本空间信息,建立起输入输出变量之间的非线性关系。与其他非线性PLS方法不同,KPLS方法只需要进行线性运算,从而避免非线性优化问题。在对过程进行监控时,首先采用KPLS方法建立模型,得到得分向量,然后计算出T²和SPE统计量及其相应的控制限。Tennessee Eastman(TE)模型上的仿真研究结果表明,所提方法比线性PLS相似文献   

基于动态单类随机森林的非线性过程监控方法

针对高维化工过程中存在的非线性和动态特性,提出了一种基于动态单类随机森林(dynamic one-class random forest,DOCRF)的过程监控方法。对正常运行状态下的过程数据进行稀疏性分析,根据其反分布产生离群点数据。利用典型变量分析对正常数据进行相关性分析,分别将正常数据和离群点数据投影到典型变量空间,利用典型变量空间数据训练单类随机森林。基于单类随机森林模型根据待检测样本与正常数据的相似度构造监控统计量进行故障检测。在Tennessee Eastman过程的仿真结果表明,所提DOCRF方法总体优于单类支持向量机方法。     

基于动态单类随机森林的非线性过程监控方法

针对高维化工过程中存在的非线性和动态特性，提出了一种基于动态单类随机森林（dynamic one-class random forest，DOCRF（的过程监控方法。对正常运行状态下的过程数据进行稀疏性分析，根据其反分布产生离群点数据。利用典型变量分析对正常数据进行相关性分析，分别将正常数据和离群点数据投影到典型变量空间，利用典型变量空间数据训练单类随机森林。基于单类随机森林模型根据待检测样本与正常数据的相似度构造监控统计量进行故障检测。在Tennessee Eastman过程的仿真结果表明，所提DOCRF方法总体优于单类支持向量机方法。     

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。     

故障分离——一种基于FDA-SVDD的模式分类算法

为了克服多变量统计过程控制在故障分离上的缺陷, 提出了一种新的故障分离方法。 新方法由基于Fisher判别分析（FDA）的特征提取、Fisher线性分类和基于支持向量数据描述（SVDD）的非线性核空间模式分类等算法组成。构造了基于FDA-SVDD的串级和混联融合方式, 并设计了基于SVDD的加权归一化半径模式判别准则。非等温连续搅拌槽 （CSTR）过程仿真验证了混联融合比单纯的FDA分类算法和串级融合具有更优良的故障分离效果。     

Nonlinear Real‐time Process Monitoring and Fault Diagnosis Based on Principal Component Analysis and Kernel Fisher Discriminant Analysis

The aim of this paper is to propose a novel real‐time process monitoring and fault diagnosis method based on the principal component analysis (PCA) and kernel Fisher discriminant analysis (KFDA). There is a need to develop this method in order to overcome the inherent limitations of the current kernel FDA method. The idea of the method is to initially reduce dimensionality using PCA and then to map the score data in the reduced original space to the high‐dimensional feature space via a nonlinear kernel function. Following this, the optimal Fisher feature vector and discriminant vector are extracted to perform process monitoring. If faults occur, the method uses the degree of similarity between the optimal discriminant vector presented and the optimal discriminant vector of the faults in the historical dataset to perform a diagnosis. The proposed method can effectively capture nonlinear relationships in process variables. In comparison with kernel FDA, the PCA plus kernel FDA method is more efficient and has a more rapid response when used to undertake online monitoring and fault diagnosis. In this study, the method is evaluated by applying it to the fluid catalytic cracking unit (FCCU) process. As a consequence, its effectiveness is demonstrated.     

基于Fisher子空间特征提取的间歇过程监控和故障诊断

1 INTRODUCTION Process monitoring and fault diagnosis are the most important tasks that determine the successful operation and the final product quality. In batch proc- ess, small changes in the operating conditions may impact the final product quality, which is often exam- ined off-line in a laboratory. If the quality variable does not satisfy a specified criterion, then it is not possible to examine the causes of fault and the time of its occurrence[1]. Therefore, early fault detection …     

Balanced multiple weighted linear discriminant analysis and its application to visual process monitoring

Visual process monitoring is important in complex chemical processes. To address the high state separation of industrial data, we propose a new criterion for feature extraction called balanced multiple weighted linear discriminant analysis(BMWLDA). Then, we combine BMWLDA with self-organizing map(SOM) for visual monitoring of industrial operation processes. BMWLDA can extract the discriminative feature vectors from the original industrial data and maximally separate industrial operation states in the space spanned by these discriminative feature vectors. When the discriminative feature vectors are used as the input to SOM, the training result of SOM can differentiate industrial operation states clearly.This function improves the performance of visual monitoring. Continuous stirred tank reactor is used to verify that the class separation performance of BMWLDA is more effective than that of traditional linear discriminant analysis, approximate pairwise accuracy criterion, max–min distance analysis, maximum margin criterion, and local Fisher discriminant analysis. In addition, the method that combines BMWLDA with SOM can effectively perform visual process monitoring in real time.     

Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map （SOM） with Fisher discriminant analysis （FDA） is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman （TE） process is em- ployed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.     

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Fisher判别分析和核回归的质量监控和估计

A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is under normal condition, then kernel regression is further used for quality prediction and estimation. If faults have occurred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can effectively detect the fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.     

A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes

A nonlinear kernel Gaussian mixture model (NKGMM) based inferential monitoring method is proposed in this article for chemical process fault detection and diagnosis. Aimed at the multimode non-Gaussian process with within-mode nonlinearity, the developed NKGMM approach projects the operating data from the raw measurement space into the high-dimensional kernel feature space. Thus the Gaussian mixture model can be estimated in the feature space with each component satisfying multivariate Gaussianity. As a comparison, the conventional independent component analysis (ICA) searches for the non-Gaussian subspace with maximized negentropy, which is not equivalent to the multi-Gaussianity in multimode process. The regular Gaussian mixture model (GMM) method, on the other hand, assumes the Gaussianity of each cluster in the original data space and thus cannot effectively handle the within-mode nonlinearity. With the extracted kernel Gaussian components, the geometric distance driven inferential index is further derived to monitor the process operation and detect the faulty events. Moreover, the kernel Gaussian mixture based inferential index is decomposed into variable contributions for fault diagnosis. For the simulated multimode wastewater treatment process, the proposed NKGMM approach outperforms the ICA and GMM methods in early detection of process faults, minimization of false alarms, and isolation of faulty variables of nonlinear and non-Gaussian multimode processes.