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。     

基于多向核熵成分分析的微生物发酵间歇过程监测研究

针对微生物发酵间歇过程监测算法只考虑数据信息最大化未考虑数据簇结构信息的不足,提出了基于多向核熵成分分析(Multi-way Kernel Entropy Component Analysis,MKECA)间歇过程监测的新方法。该方法首先引入AT展开策略对三维历史数据进行预处理,然后通过核映射将数据从低维空间映射到高维特征空间,解决数据的非线性特性,并在高维特征空间依据核熵的大小对数据进行降维,使降维后的数据能够最大化地保留原始数据的分布;同时理论证明了所提方法在特定条件下等同于传统方法,也就是说MKECA既能兼顾传统方法的优势,又能弥补传统方法的不足;最后通过青霉素仿真数据进行验证,表明MKECA方法具有更可靠的监控性能,能及时、准确地监测出故障。     

基于扩散距离的信息熵FCM多阶段LSTM-AE间歇过程故障监测

针对间歇过程中因忽略数据在阶段划分中的非线性,导致故障监测精度低的问题,提出一种基于扩散距离的信息熵模糊C均值(DDEFCM)多阶段长短期记忆网络的自动编码器(LSTM-AE)间歇过程故障监测方法。首先为了自动识别聚类个数,利用信息熵描述批处理后的二维时间片矩阵。再采用扩散距离对模糊C均值聚类(FCM)算法进行改进,解决欧式距离不能表征数据非线性的问题,有效划分间歇过程的稳定阶段,然后利用轮廓系数划分过渡阶段。最后建立多阶段LSTM-AE监测模型。利用青霉素发酵数据和大肠杆菌实际生产数据对该方法进行验证,结果表明所提方法不仅可以提升阶段划分性能,还能更加准确地进行故障监测。     

基于MKECA的非高斯性和非线性共存的间歇过程监测

多向核独立成分分析（multiway kernel independent component analysis,MKICA）在监测间歇过程非高斯性和非线性方面取得了广泛应用,其仅仅是将线性独立成分分析（independent component analysis,ICA）方法利用核主成分分析（kernel principal component analysis,KPCA）白化扩展到非线性领域,但数据经KPCA白化后只考虑数据信息最大化未考虑数据簇结构信息的不足,为解决此问题,采用核熵成分分析（kernel entropy component analysis,KECA）代替KPCA白化的过程监测方法。该方法首先利用AT展开方法将过程三维数据变为二维数据;其次用KECA进行白化处理的同时解决数据的非线性;然后建立ICA监测模型用于非高斯生产过程监测;最后将该方法应用到青霉素发酵仿真和实际的工业过程并与MKICA方法进行对比,验证该方法的有效性。     

基于核熵独立成分分析的故障检测方法

传统核独立成分分析（KICA）依据特征值的大小进行降维,但是特征值大并不一定取得的信息熵贡献度也是最大的。针对这个问题,提出一种基于核熵独立成分分析（KEICA）的故障检测方法。将训练数据集投影在高维核空间,通过对数据信息熵的贡献大小选取核主成分,并建立独立成分分析（ICA）模型。对训练样本求{\mathit{I}}^{\mathrm{2}}和\mathrm{S}\mathrm{P}\mathrm{E}统计量,并利用核密度估计计算统计量的控制限。计算测试数据对训练数据的核矩阵,将其投影在ICA模型上并计算测试样本的统计量,统计量超出控制限的样本即可被识别为故障样本。将该方法用于非线性数值例子和Tennessee Eastman（TE）过程的故障检测,并与传统的核主成分分析（KPCA）、核熵成分分析（KECA）和KICA方法进行对比,表明KEICA的监测效果优于其他三种方法。     

基于核熵独立成分分析的故障检测方法

传统核独立成分分析（KICA）依据特征值的大小进行降维,但是特征值大并不一定取得的信息熵贡献度也是最大的。针对这个问题,提出一种基于核熵独立成分分析（KEICA）的故障检测方法。将训练数据集投影在高维核空间,通过对数据信息熵的贡献大小选取核主成分,并建立独立成分分析（ICA）模型。对训练样本求{\mathit{I}}^{\mathrm{2}}和\mathrm{S}\mathrm{P}\mathrm{E}统计量,并利用核密度估计计算统计量的控制限。计算测试数据对训练数据的核矩阵,将其投影在ICA模型上并计算测试样本的统计量,统计量超出控制限的样本即可被识别为故障样本。将该方法用于非线性数值例子和Tennessee Eastman（TE）过程的故障检测,并与传统的核主成分分析（KPCA）、核熵成分分析（KECA）和KICA方法进行对比,表明KEICA的监测效果优于其他三种方法。     

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。     

基于多尺度动态核主元分析的化工过程故障检测

针对化工过程数据的多尺度性和非线性特性,提出一种多尺度动态核主元分析方法.使用小波变换分析数据的多尺度特性,借助核函数来解决非线性映射问题,同时解决了噪声和干扰造成的各变量数据具有时间序列动态性问题.在此基础上,提出一种基于矩阵相似度量的核函数参数选优方法.将上述方法应用于TE模型的故障检测过程中,仿真结果表明,该方法提高了过程性能监视和故障检测的准确性,优于线性主元分析法的检测效果.     

基于核独立元分析的间歇过程在线监控

针对间歇过程独特的数据特点,提出了一种基于核独立元分析(kernelICA)的局部在线建模监控方法。核独立元分析通过规范相关性将比较函数扩展到一个再生的核希尔伯特空间,并用核的方法在此空间对比较函数进行计算和寻优。对含有多种分布的过程源数据,核独立元分析是一种比独立元分析(ICA)更有效的特征提取方法。对于按批次方向展开的间歇过程历史建模数据,在每一个时间间隔点应用核独立元分析算法提取独立元用于建模,并计算I2和SPE统计量及相应的控制限。此方法不需要对未来测量值进行估计,更重要的是解决了核独立元分析不能直接处理间歇过程高维历史建模数据的难题。仿真结果验证了所提出方法的可行性和有效性,并显示出比传统MICA更好的监控效果。     

基于MSPCA-KECA的冷水机组故障监测及诊断

针对冷水机组同类型不同等级故障的变量变化存在差异会造成误诊断的问题,提出一种基于多尺度主元分析-核熵成分分析（MSPCA-KECA）的故障诊断策略。MSPCA提取故障特征,其输出作为KECA分类器的输入,实现故障的实时监测与自动诊断。首先,改进的MSPCA算法通过将小波多尺度分析与主元分析相结合,筛选故障信息可能存在的尺度直接重构并采用PCA提取故障特征,获取不同类型故障之间差异的同时也保留了同类型但不同等级故障之间的相似性,提高故障诊断的可靠性。之后建立KECA非线性分类器并引入一种新的监测统计量--散度测度统计量,使降维后不同特征信息之间呈现显著的角度差异,易于分类。最后,采用支持向量数据描述（SVDD）算法确定新统计量的控制限,以克服无法获知统计量分布的问题。通过对冷水机组数据的仿真研究,验证了MSPCA-KECA方法的可行性及有效性。     

自适应MPCA方法在链霉素过程监控中的应用

Multi-way principal component analysis (MPCA) had been successfully applied to monitoring the batch and semi-batch process in most chemical industry. An improved MPCA approach, step-by-step adaptive MPCA (SAMPCA), using the process variable trajectories to monitoring the batch process is presented in this paper. It does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The approach is based on a MPCA method that processes the data in a sequential and adaptive manner. The adaptive rate is easily controlled through a forgetting factor that controls the weight of past data in a summation. This algorithm is used to evaluate the industrial streptomycin fermentation process data and is compared with the traditional MPCA. The results show that the method is more advantageous than MPCA, especially when monitoring multi-stage batch process where the latent vector structure can change at several points during the batch.     

Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

The kernel principal component analysis (KPCA) method employs the first several kernel principal components (KPCs), which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis (SKPCA) is proposed to improve process monitoring performance, i.e., to deal with the discordance of T² statistic and squared prediction error δ_SPE statistic and reduce missed detection rates. T² statistic can be used to measure the variation directly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T² statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.     

Fault Detection of Non‐Linear Processes Using Kernel Independent Component Analysis

In this paper, a new non‐linear process monitoring method based on kernel independent component analysis (KICA) is developed. Its basic idea is to use KICA to extract some dominant independent components capturing non‐linearity from normal operating process data and to combine them with statistical process monitoring techniques. The proposed method is applied to the fault detection in the Tennessee Eastman process and is compared with PCA, modified ICA, and KPCA. The proposed approach effectively captures the non‐linear relationship in the process variables and showed superior fault detectability compared to other methods while attaining comparable false alarm rates.     

On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis

In the past decades, on-line monitoring of batch processes using multi-way independent component analysis (MICA) has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and determining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components, we introduce a novel concept of system deviation, which is able to evaluate the reconstructed observations with different independent components. The monitored statistics are transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-batch penicillin fermentation simulator, and the experimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods.     

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。     

A Robust Statistical Batch Process Monitoring Framework and Its Application

In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to latent structure (MPLS) were proposed for on-line batch process monitoring. However, they are based on the decomposition of relative covariance matrix and strongly affected by outlying observations. In this paper, in view of an efficient projection pursuit algorithm, a robust statistical batch process monitoring (RSBPM) framework, which is resistant to outliers, is proposed to reduce the high demand for modeling data. The construction of robust normal operating condition model and robust control limits are discussed in detail. It is evaluated on monitoring an industrial streptomycin fermentation process and compared with the conventional MPCA. The results show that the RSBPM framework is resistant to possible outliers and the robustness is confirmed.     

一种基于改进MPCA的间歇过程监控与故障诊断方法

针对基于不同展开方式的多向主元分析(MPCA)方法在线应用时各自存在的缺陷,提出一种改进的基于变量展开的MPCA方法,实现间歇过程的在线监控与故障诊断。该方法采用随时间更新的主元协方差代替固定的主元协方差进行T²统计量的计算,充分考虑了主元得分向量的动态特性;同时引入主元显著相关变量残差统计量,避免SPE统计量的保守性,且该统计量能提供更详细的过程变化信息,对正常工况改变或过程故障引起的T²监控图变化有一定的识别能力;最后提出一种随时间变化的贡献图计算方法用于在线故障诊断。该方法和MPCA方法的监控性能在一个青霉素发酵仿真系统上进行了比较。仿真结果表明：该方法具有较好的监控性能,能及时检测出过程存在的故障,且具有一定的故障识别和诊断能力。     

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.