一种基于聚类方法的多阶段间歇过程监控方法

针对阶段不等长的多阶段间歇过程,提出了一种基于k-均值聚类方法的阶段分段策略,可以将不等长的阶段准确分类。首先,将间歇过程的三维训练数据按变量方向展开成二维矩阵,再通过k-均值聚类的方法按照相关性将数据聚成多类并运用主元分析（PCA）方法分别对每一类建立模型。在线监控时,通过计算样本与模型之间的相似系数以选择最合适的模型进行在线监控。此方法可以将不同批次在同一采样时刻的过程数据按照相关性分到多个阶段,更符合生产过程中常见的过程数据阶段不等长的情况。最后利用青霉素仿真验证了该方法的有效性。     

基于仿射传播聚类子集主元分析的间歇过程监测方法

针对间歇过程固有的多阶段特性,也为了克服传统阶段划分方法严格按照物理时刻顺序将采样点硬性分割而不能使其寻找数据特征最为相近的聚类中心的严重缺陷,提出基于仿射传播聚类(AP)的子集多向主元分析(subset-MPCA)监测新方法:采用全新的乱序聚类思想,将时间片矩阵打乱用AP进行无约束乱序聚类,使样本突破时间顺序的约束自由找寻与其特征最为相近的聚类中心,获得聚类子集,建立精确的子集MPCA监控模型。在线监控时,引入信息度传递实现实时采样点的阶段归属判断,解决阶段不等长批次的最佳模型选择问题。对青霉素仿真数据的实验表明,该方法较传统方法可有效降低故障的漏报和误报,有着更加可靠的监控性能。     

基于多阶段多向核熵成分分析的间歇过程故障检测方法

针对间歇过程的非线性、多阶段特性,提出一种基于多阶段多向核熵成分分析（multistage-MKECA,MsMKECA）的故障检测方法。针对间歇过程的多阶段特性,建立一种时序核熵主元关联度的矩阵相似性阶段划分方法,实现对间歇生产过程的多阶段划分;针对传统批次展开方式在线监控需要预估批次未来值的缺陷,进一步引入一种批次-变量三维数据展开方式建立每个阶段的MKECA非线性统计模型,实现对间歇过程的分阶段监控。最后对盘尼西林发酵过程开展仿真研究,结果表明所提方法能够比传统MKECA方法更为快速地进行故障检测。     

基于多阶段多向核熵成分分析的间歇过程故障检测方法

针对间歇过程的非线性、多阶段特性,提出一种基于多阶段多向核熵成分分析(multistage-MKECA,Ms MKECA)的故障检测方法。针对间歇过程的多阶段特性,建立一种时序核熵主元关联度的矩阵相似性阶段划分方法,实现对间歇生产过程的多阶段划分;针对传统批次展开方式在线监控需要预估批次未来值的缺陷,进一步引入一种批次-变量三维数据展开方式建立每个阶段的MKECA非线性统计模型,实现对间歇过程的分阶段监控。最后对盘尼西林发酵过程开展仿真研究,结果表明所提方法能够比传统MKECA方法更为快速地进行故障检测。     

基于多向核熵成分分析的微生物发酵过程多阶段划分及故障监测

针对多向核主元分析法(MKPCA)在监控动态非线性和多模态间歇生产过程故障的不足,提出一种基于物理信息熵的多阶段多向核熵成分分析(multiple sub-stage multi-way kernel entropy component analysis,MSMKECA)的新方法用于故障监控。该方法首先通过核映射将数据从低维空间映射到高维特征空间;其次在高维特征空间依据熵结构信息计算每个时刻数据矩阵的相似度指标进行阶段划分,将间歇过程划分为各稳定阶段和各过渡阶段,并在过渡阶段用时变的协方差代替固定协方差;最后在划分的阶段里分别建立模型进行间歇过程监测解决间歇过程的动态非线性和多阶段特性;将所提出的算法应用于青霉素发酵仿真系统的在线监测,验证了该方法的有效性。     

基于仿射传播聚类子集主元分析的间歇过程监测方法

针对间歇过程固有的多阶段特性,也为了克服传统阶段划分方法严格按照物理时刻顺序将采样点硬性分割而不能使其寻找数据特征最为相近的聚类中心的严重缺陷,提出基于仿射传播聚类(AP)的子集多向主元分析(subset-MPCA)监测新方法:采用全新的乱序聚类思想,将时间片矩阵打乱用AP进行无约束乱序聚类,使样本突破时间顺序的约束自由找寻与其特征最为相近的聚类中心,获得聚类子集,建立精确的子集MPCA监控模型。在线监控时,引入信息度传递实现实时采样点的阶段归属判断,解决阶段不等长批次的最佳模型选择问题。对青霉素仿真数据的实验表明,该方法较传统方法可有效降低故障的漏报和误报,有着更加可靠的监控性能。     

PDPSO优化多阶段AR-PCA间歇过程监测方法

针对间歇过程固有的多阶段特性和动态性,提出基于种群多样性的自适应惯性权重粒子群算法（PDPSO）优化的多阶段自回归主元分析（AR-PCA）间歇过程监测方法。该方法引入了PDPSO算法指导AP聚类偏向参数的选取,避免了传统方法依据聚类评价指标选取参考度时的盲目性。对PDPSO优化AP聚类的多阶段发酵过程的数据样本建立AR-PCA模型能够消除各阶段的动态性及变量之间的自相关和互相关影响。最后,对自回归（AR）模型的残差矩阵建立主成分分析（PCA）模型用于发酵过程监测。将该方法应用到青霉素发酵过程,并与传统方法进行对比,结果表明,该方法能够有效进行间歇过程阶段划分并降低故障的漏报和误报。     

PDPSO优化多阶段AR-PCA间歇过程监测方法

针对间歇过程固有的多阶段特性和动态性,提出基于种群多样性的自适应惯性权重粒子群算法(PDPSO)优化的多阶段自回归主元分析(AR-PCA)间歇过程监测方法。该方法引入了PDPSO算法指导AP聚类偏向参数的选取,避免了传统方法依据聚类评价指标选取参考度时的盲目性。对PDPSO优化AP聚类的多阶段发酵过程的数据样本建立AR-PCA模型能够消除各阶段的动态性及变量之间的自相关和互相关影响。最后,对自回归(AR)模型的残差矩阵建立主成分分析(PCA)模型用于发酵过程监测。将该方法应用到青霉素发酵过程,并与传统方法进行对比,结果表明,该方法能够有效进行间歇过程阶段划分并降低故障的漏报和误报。     

一种基于可预测元分析的故障诊断方法

将可预测元分析(Fore CA)引入到过程监控中,通过选取合适的可预测元并构造能够反映系统运行状况的统计量对在线数据进行统计监控,克服了主元分析(PCA)方法假设数据服从高斯分布且无法反映系统动态时序特性的缺陷,能很好地描述工业过程的动态特性并进行故障检测。TE模型上的仿真结果证明了Fore CA在工业过程监控中的可行性与有效性。     

基于约翰逊转换的鲁棒化工过程监控方法

化工厂中一个小故障可能导致大事故,从而造成生命财产损失和环境破坏。为了防止小故障演变成大事故,化学工业需要有效的过程监控来及时检测故障和诊断故障原因。传统化工过程监控方法主元分析法(Principal Component Analysis, PCA)假设数据服从高斯分布,实践中有时并不满足该条件。此外,其使用方差、协方差捕捉数据非线性变化时,鲁棒性较差。本工作提出一种改进的主元分析法—基于约翰逊转换的鲁棒过程监控方法。首先引入约翰逊正态转换(Johnson Transformation)使过程数据服从高斯分布;其次使用鲁棒性强的斯皮尔曼相关系数(Spearman Correlation Coefficient)矩阵代替传统主元分析法的协方差矩阵提取特征向量,构造特征空间;最后将过程数据投影到特征空间,使用T2和SPE统计量实施过程监控。将此方法应用于TE过程故障案例,并与PCA和核主元分析法(Kernel Principal Component Analysis, KPCA)对比,验证了此方法的有效性。     

INTRODUCTION OF DYNAMICS TO AN APPROACH FOR BATCH PROCESS MONITORING USING INDEPENDENT COMPONENT ANALYSIS

Use of independent component analysis (ICA) in developing statistical monitoring charts for batch processes has been reported previously. This article extends the previous work by introducing time lag shifts to include process dynamics in the ICA model. Comparison of the dynamic ICA-based method with other batch process monitoring approaches based on static ICA, static principal component analysis (PCA), and dynamic PCA is made for an industrial batch polymerization reactor and a simulated fed-batch penicillin fermentation process. For both case studies, it was found that the dynamic ICA approach detected faults earlier than other approaches, with less ambiguity, and was the only approach that detected all the faults.     

INTRODUCTION OF DYNAMICS TO AN APPROACH FOR BATCH PROCESS MONITORING USING INDEPENDENT COMPONENT ANALYSIS

Use of independent component analysis (ICA) in developing statistical monitoring charts for batch processes has been reported previously. This article extends the previous work by introducing time lag shifts to include process dynamics in the ICA model. Comparison of the dynamic ICA-based method with other batch process monitoring approaches based on static ICA, static principal component analysis (PCA), and dynamic PCA is made for an industrial batch polymerization reactor and a simulated fed-batch penicillin fermentation process. For both case studies, it was found that the dynamic ICA approach detected faults earlier than other approaches, with less ambiguity, and was the only approach that detected all the faults.     

Nonlinear dynamic process monitoring based on dynamic kernel PCA

Nonlinear dynamic process monitoring based on dynamic kernel principal component analysis (DKPCA) is proposed. The kernel functions used in kernel PCA (KPCA) are profitable for capturing nonlinear property of processes and the time-lagged data extension is suitable for describing dynamic characteristic of processes. DKPCA enables us to monitor an arbitrary process with severe nonlinearity and (or) dynamics. In this respect, it is a generalized concept of multivariate statistical monitoring approaches. A unified monitoring index combined T² with SPE is also suggested. The proposed monitoring method based on DKPCA is applied to a simulated nonlinear process and a wastewater treatment process. A comparison study of PCA, dynamic PCA, KPCA, and DKPCA is investigated in terms of type I error rate, type II error rate, and detection delay. The monitoring results confirm that the proposed methodology results in the best monitoring performance, i.e., low missing alarms and small detection delay, for all the faults.     

Fault identification for process monitoring using kernel principal component analysis

In this research, we develop a new fault identification method for kernel principal component analysis (kernel PCA). Although it has been proved that kernel PCA is superior to linear PCA for fault detection, the fault identification method theoretically derived from the kernel PCA has not been found anywhere. Using the gradient of kernel function, we define two new statistics which represent the contribution of each variable to the monitoring statistics, Hotelling's T²and squared prediction error (SPE) of kernel PCA, respectively. The proposed statistics which have similar concept to contributions in linear PCA are directly derived from the mathematical formulation of kernel PCA and thus they are straightforward to understand. The main contribution of this work is that we firstly suggest a fault identification method especially applicable to process monitoring using kernel PCA. To demonstrate the performance, the proposed method is applied to two simulated processes, one is a simple nonlinear process and the other is a non-isothermal CSTR process. The simulation results show that the proposed method effectively identifies the source of various types of faults.     

Online Flooding Supervision in Packed Towers: An Integrated Data‐Driven Statistical Monitoring Method

The development of simple and efficient monitoring methods for flooding supervision is an important but difficult task for the safe operation of packed towers. A data‐driven online flooding monitoring method named Bayesian integrated dynamic principal component analysis (IDPCA) is assessed. In the first step of IDPCA, using the fuzzy c‐means clustering method, the multivariate samples collected during plant operation are first classified into several groups. Then, in each subset a dynamic principal component analysis (DPCA) model is constructed to extract the process characteristics. To improve the monitoring performance, Bayesian inference is utilized to combine these DPCA models in a suitable manner. Consequently, the control limits are formulated using the probabilistic analysis. The superiority of IDPCA is illustrated using a lab‐scale packed tower by comparison with the conventional principal component analysis (PCA) and DPCA methods.     

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.     

基于双层局部KPCA的非线性过程微小故障检测方法

针对传统核主元分析（KPCA）方法难以有效检测微小故障的问题,提出一种基于双层局部核主元分析（double-level local kernel principal component analysis,DLKPCA）的非线性过程微小故障检测方法。该方法从变量和样本两个角度来挖掘数据内部的局部信息,以提高故障检测能力。首先,利用变量分块思想,基于不同变量与核主元之间互信息相关度的相似性,将所有过程变量划分多个局部变量块。然后,构建基于得分向量和特征值的残差函数以挖掘样本局部信息。最后利用贝叶斯融合策略对各块的结果进行融合。在田纳西-伊斯曼基准过程的仿真结果表明,在微小故障检测方面,本文所提方法具有比传统KPCA方法更好的故障检测性能。     

基于Gath-Geva算法和核极限学习机的多阶段间歇过程软测量

间歇过程具有较强的非线性,多阶段、慢时变及批次间存在变化,采用单一预测模型不能反映间歇过程的多阶段特性及阶段间过渡特性。提出一种基于Gath-Geva聚类和核极限学习机（kernel extreme learning machine,KELM）的多模型软测量方法。首先采用主成分分析（principal component analysis,PCA）对输入做特征提取,然后利用Gath-Geva算法对间歇过程进行多阶段工况划分,根据生产工况特性划分为不同的操作阶段后,分别建立局部KELM模型。对任一待预测样本,分别计算其对应各局部模型的预测值,最后采用贝叶斯集成,将其隶属于各局部模型的模糊隶属度作为权重和预测值融合得到最终预测值。以青霉素发酵数据进行实验测试,结果表明所提多模型算法相较于单一模型,具有更高的预测精度。