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。     

基于长短时记忆循环网络的塑料编织机故障诊断研究

长短时记忆(LSTM)循环神经网络的塑料编织机故障诊断法通过提取振动信号的能量矩,突出信号在时间轴上的分布特征,降低输入模型的向量维度。从多个特征向量构成的样本集中选择80%作为训练样本,训练LSTM循环神经网络模型,并利用剩余样本验证模型的检测精度;以准确率、查准率和查全率作为评价指标,利用多组不同的振动数据样本,对BP神经网络模型、卷积神经网络(CNN)模型和LSTM循环神经网络模型进行比较分析。结果表明:LSTM循环神经网络模型在不同样本中能够同时达到较高的准确率、查准率和查全率,其平均值分别可达95.69%、86.96%、96.89%,证明LSTM循环神经网络能充分学习具有时序特性的故障信息,对塑料编织机的故障诊断具有可行性和有效性。     

基于MIC-CFS-LSTM的SCR出口NOx浓度动态预测

针对燃煤机组选择性催化还原(SCR)系统出口氮氧化物(NO_x)预测模型精度不高的问题，提出一种基于最大信息系数(MIC)和长短期记忆(LSTM)神经网络的预测模型方法。首先采用MIC估计各变量的延迟时间，对数据进行时延重构；然后采用重构后数据的MIC值作为评价各输入变量和输出变量间相关性大小的指标，并结合基于关联性的特征选择算法(CFS)进行输入变量筛选；最后基于时延重构和变量筛选后的数据，采用LSTM神经网络建立了SCR出口氮氧化物浓度动态预测模型。该模型被用于广东某320 MW燃煤机组实际运行数据分析。结果表明，经时延重构和变量筛选后所建立的LSTM预测模型具有较高精度，优于深度神经网络(DNN)模型和径向基函数(RBF)神经网络模型，平均绝对百分比误差达2.58%,均方根误差达2.02,可满足现场运用要求。     

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。     

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.     

基于注意力动态卷积自编码器的发酵过程故障监测

发酵过程的状态监测对于及时发现各类异常故障起到了至关重要的作用。然而，由于发酵过程数据呈现非线性特性，导致在提取特征信息时存在困难，增加了故障监测的难度。为了解决上述问题，提出了一种基于注意力动态卷积自编码器（attention dynamic convolutional autoencoder, ADCAE）的发酵过程故障监测方法。首先，设计了一种动态卷积结构（dynamic convolution structure），该结构可以在浅层使用大尺寸卷积核提取低级特征，在深层使用小尺寸卷积核提取高级特征，从而拓宽了模型特征学习的尺度；其次，设计了一种通道卷积注意力（channel convolutional attention, CCA）模块，该模块能够从不同尺度提取输入的非线性特征，并且在通道向量转化为权重的过程中可以更好地提取局部特征，提高了对有效信息的关注能力；最后，将动态卷积结构与CCA模块融入卷积自编码器中，使模型能够有效地捕获变量中的非线性关系，从而更好地应对发酵过程中的故障监测问题。利用青霉素发酵过程仿真平台和大肠埃希菌实际生产数据对该方法的可行性进行了验证，结果表明该方...     

基于GSA-LSTM动态结构特征提取的间歇过程监测方法

间歇过程监测对于保证批次生产过程的稳定运行具有重要意义。传统过程监测方法难以提取间歇过程数据特有的非线性结构和动态时变特征。为此,提出了一种融合图采样聚合网络和长短期记忆网络(GSA-LSTM)的典型相关分析方法用于间歇过程在线监测。首先,利用K近邻方法将批次过程数据转化为图结构形式,利用图采样聚合网络(GraphSAGE)提取数据内部的结构特征,然后利用长短期记忆网络(LSTM)提取数据的非线性动态特征,通过权重系数将结构特征和动态特征融合得到更具有代表性的间歇过程数据特征。进一步地,利用典型相关分析方法对残差建立监测模型。最后将所提方法应用于数值例子和注塑过程监测,结果分析验证了所提方法的有效性。     

基于GSA-LSTM动态结构特征提取的间歇过程监测方法

间歇过程监测对于保证批次生产过程的稳定运行具有重要意义。传统过程监测方法难以提取间歇过程数据特有的非线性结构和动态时变特征。为此,提出了一种融合图采样聚合网络和长短期记忆网络(GSA-LSTM)的典型相关分析方法用于间歇过程在线监测。首先,利用K近邻方法将批次过程数据转化为图结构形式,利用图采样聚合网络(GraphSAGE)提取数据内部的结构特征,然后利用长短期记忆网络(LSTM)提取数据的非线性动态特征,通过权重系数将结构特征和动态特征融合得到更具有代表性的间歇过程数据特征。进一步地,利用典型相关分析方法对残差建立监测模型。最后将所提方法应用于数值例子和注塑过程监测,结果分析验证了所提方法的有效性。     

基于张量分解的间歇过程故障诊断方法

针对间歇过程的三维数据特点和常出现的渐变故障,提出一种基于张量分解的故障诊断方法:累加和的张量主元分析(summed tensor principal component analysis, STPCA)。该方法先结合累积和控制图(CUSUM)对三维样本数据进行累加处理,累积叠加历史信息,然后利用张量分解思想直接对三维数据进行TPCA分解得到投影矩阵U和V,避免传统方法在展开成二维数据过程中破坏原有数据结构问题,最后构造监测统计量,求取置信限建立故障诊断模型。在盘尼西林发酵仿真实验中,将多向主元分析(MPCA)和基于张量分解的TPCA、STPCA方法比较,得出结论:针对过程的跳变故障,TPCA方法检测故障准确有效,对于渐变故障,基于STPCA的过程监控方法故障检测性能更突出。     

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。     

Multi-stage fusion regression network for quality prediction of batch process

In most batch processes, the correlations of process variables present multi-stage characteristic as the process progress and operating conditions change. The methods building a local model at each stage ignore the potential correlations among stages, resulting in poor quality prediction of batch process. To solve this problem, a batch process quality prediction method based on multi-stage fusion regression network (MSFRN) is proposed. First, the affine propagation clustering (AP) algorithm is used to automatically divide the stages for batch process without relying on prior knowledge. Second, the input reconstruction error and quality prediction error are organically combined to develop a stacked isomorphic and quality-driven autoencoder (SIQAE) for each stage, which fully extracts the quality-related features for each stage while reducing the input cumulative loss. Then, the self-attention mechanism is used to integrate the quality-related features of each stage so as to obtain global features which consider the correlations among stages. Finally, the global features are input into the fully connected regression layer to predict the quality variables of batch process. The effectiveness of the proposed method was verified by applying on penicillin fermentation process.     

Batch process monitoring using multiway Laplacian autoencoders

In this paper, a novel nonlinear method named multiway Laplacian autoencoder (MLAE) is proposed for batch process monitoring. Autoencoder (AE) is an effective unsupervised learning neural network for nonlinear feature extraction. Compared with traditional AEs, the proposed method has two main advantages. Firstly, traditional AEs usually ignore the local structure of the original dataset. The proposed MLAE method integrates graph Laplacian regularization to the loss function, and, thus, the local structure of the normal process data is fully considered. Secondly, the Laplacian matrix of the regularization term is constructed by an average local affinity matrix of all batch runs, which contains the information of the stochastic deviations among batches. Furthermore, two statistics, ie, H² and SPE statistics, are developed based on the extracted hidden representation and the retained reconstruction error. The effectiveness and advantages of the MLAE-based monitoring strategy are illustrated by a benchmark penicillin fermentation process and a real E. coli fermentation process.     

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。     

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.     

Dynamic nonlinear batch process fault detection and identification based on two‐directional dynamic kernel slow feature analysis

The batch process generally covers high nonlinearity and two‐directional dynamics: time‐wise dynamics, which correspond to inherently time‐varying dynamics resulting from the slowly varying underlying driving forces within each batch duration; and batch‐wise dynamics, which are associated with different operating modes among different batches. However, most existing dynamic nonlinear monitoring methods cannot extract the slowly varying underlying driving forces of the nonlinear batch process and rarely tackle the batch‐wise dynamic characteristics among batch runs. In order to address these issues, a new monitoring scheme based on two‐directional dynamic kernel slow feature analysis (TDKSFA) is developed by combining kernel SFA with a global modelling strategy. In the TDKSFA method, kernel SFA is integrated with the ARMAX time series model to mine the nonlinear and time‐wise dynamic properties within a batch run due to its capability of extracting the slowly varying underlying driving forces. Furthermore, the global modelling strategy is presented to handle the batch‐wise dynamics among batches by calculating the total average kernel matrix of all training batches. After the slow features are extracted, Hotelling's T² and SPE statistics are built to detect faults. To solve the issue of fault variable nonlinear identification, a novel nonlinear contribution plot inspired by the pseudo‐sample variable projection trajectories in the TDKSFA model is further proposed to identify fault variables. Finally, the feasibility and effectiveness of the TDKSFA‐based fault diagnosis strategy is demonstrated through a numerical system and the penicillin fermentation process.     

PTFE间歇聚合反应过程变周期动态经济优化控制

聚四氟乙烯(PTFE)间歇聚合生产模式可满足小批量、多用途以及高质量产品的市场需求。针对PTFE聚合过程存在强非线性和大时滞特性,提出了一种基于自由终端的动态经济优化控制方法。首先,将生产周期作为一个自由度纳入优化变量建立动态经济优化问题,采用改进控制变量参数化方法,控制输入被离散为可变长度的片状序列,便可将动态经济优化问题转换为非线性规划(NLP)问题;然后,采用基于梯度下降的内点罚函数法求解NLP问题,通过变周期预测时域的滚动优化控制方法优化控制输入和终端时间;最后将提出的变周期动态经济优化控制与传统PI控制、非线性模型预测控制进行对比测试分析,仿真结果表明本方法单位经济效益更高,生产周期更短,突显了间歇生产的灵活性。     

基于长短时记忆神经网络和支持向量数据描述的间歇过程监测方法

间歇过程操作是化工过程中的一种重要生产方式.与连续过程不同,间歇生产不是在一个稳定的工作状态运行,而是根据设定的原料比例、操作条件所对应的操作轨迹运行.因此间歇过程数据具有多阶段性、动态时变性和非线性等特性,传统的监测方法难以应用于对间歇过程生产运行状态的监测.为了解决这个问题,提出了一种新的间歇过程监测策略.首先基于...