基于PSO的间歇生产鲁棒统计过程监控

间歇过程广泛应用于精细化工产品、生物化工产品等高附加值产品的制备.为提高间歇生产的可重复性,提高批次之间产品的一致性,多向主元分析法(MPCA)广泛应用于间歇生产过程的监控.针对MPCA统计监控模型容易受到建模数据中离群点影响的不足,提出了一种基于微粒群优化算法(PSO)的鲁棒MPCA分析方法,并进一步给出了相应鲁棒监控统计量的计算方法.对于链霉素发酵过程的监控表明,相对于普通MPCA,鲁棒MPCA在建模数据中存在离群点时仍能够给出正确的统计监控模型,从而有效减少了建模过程对数据的要求.     

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。     

MPCA在间歇反应过程故障诊断中的应用

将多方向主元分析(MPCA)理论应用到一个实际的PVC间歇反应过程的性能监测与故障诊断中。由于间歇反应的特点,数据具有多维性,应用传统的主元分析将使过程的统计建模与故障诊断难以实现。MPCA可将间歇过程的多维数据沿时间轨迹分割,使得多批次的数据可以在各时间序列轨迹上建立相应的PCA模型,从而完成对间歇过程的实时监视及故障诊断。     

步进MPCA及其在间歇过程监控中的应用

针对多向主元分析法(MPCA)在间歇过程监控过程中需要预测过程未来输出的困难,提出了一种新的步进多向主元分析方法。该方法通过建立一系列的PCA模型,避免了对预估过程变量未来输出的需要,通过引入遗忘因子能够自然地处理多阶段间歇过程的情况。对于多阶段链霉素发酵过程的监控表明,相对于普通MPCA,步进MPCA能够更精确地对过程故障行为进行描述。     

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。     

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.     

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。     

基于主元分析的FPSO故障检测与诊断

应用基于主元分析的故障诊断方法对浮式油轮生产储油卸油系统(FPSO)进行故障检测与诊断研究.选取FPSO油气水分离系统的18个主要过程监控变量为研究对象,通过对系统历史数据进行预处理分析,建立主元模型;利用主元模型对仿真实时数据进行故障检测,应用SPE统计法和Hotelling统计法判断系统是否发生故障;使用贡献图法实现故障分离.研究结果表明:基于主元分析的故障诊断方法可以准确地对FPSO生产过程的早期故障进行检测和诊断;且对于系统的细小扰动,动态主元分析法的故障诊断能力优于主元分析法.     

基于中心损失的条件生成式对抗网络的冷水机组故障诊断

针对冷水机组产生的故障数据不足,数据集中正常数据和故障数据数量不平衡,进而导致故障诊断精度下降的问题,提出一种基于中心损失的条件生成式对抗网络(central loss conditional generative adversarial network,CLCGAN)和支持向量机(support vector machine,SVM)的故障诊断方法。首先,CLCGAN利用少量真实故障数据生成新的故障数据;然后,将生成的故障数据与初始数据集混合,使正常数据与故障数据的数量达到平衡;最后,利用平衡数据集构建SVM模型进行故障诊断。在GAN生成冷水机组故障数据时,构建动态中心损失项并加入到目标函数中,利用动态的中心损失减少冷水机组生成的各种故障数据的类内距离,从而降低各个故障生成数据之间的重叠程度,增加生成数据的可靠性。在生成故障数据之前配置相应的故障标签,并输入到CLCGAN中指导数据生成过程,使生成的故障数据可以均衡地分布于各个故障类别。在ASHRAE 1043-RP数据集上对所提方法进行了验证,结果表明,相较于其他解决数据不平衡问题的故障诊断方法,所提方法具有更高的故障诊断准确率。     

基于滚动MPCA的青霉素发酵过程的在线监控

To reduce the variations of the production process in penicillin cultivations, a rolling multivariate statistical approach based on multiway principle component analysis （MPCA） is developed and used for fault diagnosis of penicillin cultivations. Using the moving data windows technique, the static MPCA is extended for use in dynamic process performance monitoring. The control chart is set up using the historical data collected from the past successful batches, thereby resulting in simplification of monitoring charts, easy tracking of the progress in each batch run, and monitoring the occurrence of the observable upsets. Data from the commercial-scale penicillin fermentation process are used to develop the rolling model. Using this method, faults are detected in real time and the corresponding measurements of these faults are directly made through inspection of a few simple plots （t-chart, SPE-chart, and T2-chart）. Thus, the present methodology allows the process operator to actively monitor the data from several cultivations simultaneously.     

Multiway discrete hidden Markov model‐based approach for dynamic batch process monitoring and fault classification

A new multiway discrete hidden Markov model (MDHMM)‐based approach is proposed in this article for fault detection and classification in complex batch or semibatch process with inherent dynamics and system uncertainty. The probabilistic inference along the state transitions in MDHMM can effectively extract the dynamic and stochastic patterns in the process operation. Furthermore, the used multiway analysis is able to transform the three‐dimensional (3‐D) data matrices into 2‐D measurement‐state data sets for hidden Markov model estimation and state path optimization. The proposed MDHMM approach is applied to fed‐batch penicillin fermentation process and compared to the conventional multiway principal component analysis (MPCA) and multiway dynamic principal component analysis (MDPCA) methods in three faulty scenarios. The monitoring results demonstrate that the MDHMM approach is superior to both the MPCA and MDPCA methods in terms of fault detection and false alarm rates. In addition, the supervised MDHMM approach is able to classify different types of process faults with high fidelity. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Heats and input variables selection for designing a water detection framework applicable to industrial electric arc furnaces

This paper describes the development of “heats” and input variables selection models that are incorporated into a water detection framework for an industrial steelmaking electric arc furnace (EAF). The selection models in this work are developed based on latent variable methods. The latent variable methods used in this work are multiway principal component analysis (MPCA) and multiway projection to latent structures (MPLS). The particular problems related to latent variable methods discussed in this paper include data preprocessing, including alignment, unfolding method, centering, and scaling. The outcome of the heats selection model is heats with normal operation and the outcome of the input variables selection model is variables that are highly correlated with the off-gas water vapour. The water detection framework and developed models are useful in practical settings for the prediction of water leakage and the design of appropriate fault detection and diagnosis strategies.     

Multivariate monitoring for time-derivative non-Gaussian batch process

This research is an application of process monitoring on a pilot-scale sequencing batch reactor (SBR) using a batchwise multiway independent component analysis method (MICA) for denoising effect, which can extract meaningful hidden information from non-Gaussian data. Three-way batch data of SBR are unfolded batch wise, and then a multivariate monitoring method is used to capture the non-Gaussian and nonlinear characteristics of normal batches. It is successfully applied to an 80 L SBR for biological wastewater treatment, which is characterized by a variety of error sources with non-Gaussian characteristics. In the monitoring result, multiway principal component analysis (MPCA) can detect the abnormal batches with a false alarm rate of 47.5%, whereas MICA charts show less false alarm rate of 4.5%. The results of this pilot-scale SBR monitoring system using simple on-line measurements clearly demonstrated that the MICA monitoring technique showed lower false alarm rate and physically meaningful robust monitoring results.     

在线自适应批次过程监视的双滑动窗口MPCA方法

Online monitoring of chemical process performance is extremely important to ensure the safety of a chemical plant and consistently high quality of products. Multivariate statistical process control has found wide applications in process performance analysis, monitoring and fault diagnosis using existing rich historical database. In this paper, we propose a simple and straight forward multivariate statistical modeling based on a moving window MPCA （multiway principal component analysis） model along the time and batch axis for adaptive monitoring the progress of batch processes in real-time. It is an extension to minimum window MPCA and traditional MPCA. The moving window MPCA along the batch axis can copy seamlessly with variable run length and does not need to estimate any deviations of the ongoing batch from the average trajectories. It replaces an invariant fixed-model monitoring approach with adaptive updating model data structure within batch-to-batch, which overcomes the changing operation condition and slows time-varying behaviors of industrial processes. The software based on moving window MPCA has been successfully applied to the industrial polymerization reactor of polyvinyl chloride （PVC） process in the Jinxi Chemical Company of China since 1999.     

一种新的间歇过程故障诊断策略

间歇过程的在线故障诊断近年来受到了越来越多的关注,目前比较通用的方法主要是多变量统计的方法。然而在实际过程尤其是多阶段的间歇过程中故障诊断效果往往不够理想,误诊率比较高。为解决上述问题,本文基于动态轨迹分析（DLA）和在线的动态时间规整方法（DTW）,将二者的优点有效地结合在一起提出了一种在线故障诊断策略,提高了故障诊断效率和准确性。青霉素发酵过程的在线故障诊断应用实例表明该方法具有比较好的诊断效果。     

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.     

Development of hidden semi-Markov models for diagnosis of multiphase batch operation

This paper deals with automatic on-line detection and diagnosis of fault patterns in multiphase batch processes. A novel and flexible approach based on the combination of hidden segmental semi-Markov models (HSMM) and multiway principal component analysis (MPCA) is proposed. In all batch operations, process variables may have correlations with each other, and MPCA is used to handle cross-correlation among process variables. In multiphase batch processes, the effect of external factors on process variables is phase-specific and the duration of each phase varies from batch to batch. HSMM is used to model the multiphase batch operation by representing each phase with a macro-state whose duration is determined by a phase-specific probability distribution of a number of micro-states. The output of each micro-state corresponds to the values of the monitored variables at a specific point in time. Given this structure, MPCA-HSMM parameters are trained by the batch operation data and recursive Viterbi algorithm is used to find out the optimum state sequence from each batch. Probability values of the optimum state sequence are collected to construct the probabilistic model which is used to compute the corresponding control limit for the specified operating condition. One MPCA-HSMM model is to be built for each type of previously known operating condition—normal and fault events. The power and advantages of the proposed method are successfully demonstrated in a simulated fed-batch penicillin cultivation process. MPCA-HSMM correctly identifies the type of fault from the batch operation data.     

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.