Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis

Latent variable (LV) models have been widely used in multivariate statistical process monitoring. However, whatever deviation from nominal operating condition is detected, an alarm is triggered based on classical monitoring methods. Therefore, they fail to distinguish real faults incurring dynamics anomalies from normal deviations in operating conditions. A new process monitoring strategy based on slow feature analysis (SFA) is proposed for the concurrent monitoring of operating point deviations and process dynamics anomalies. Slow features as LVs are developed to describe slowly varying dynamics, yielding improved physical interpretation. In addition to classical statistics for monitoring deviation from design conditions, two novel indices are proposed to detect anomalies in process dynamics through the slowness of LVs. The proposed approach can distinguish whether the changes in operating conditions are normal or real faults occur. Two case studies show the validity of the SFA‐based process monitoring approach. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3666–3682, 2015     

面向系统层级的复杂工业过程全息故障诊断

复杂工业过程具有长流程、系统层级多、故障潜在分布空间范围较广的特点,是当前故障诊断领域的热门研究方向。首先,对主流故障诊断技术进行了分类和概述;其次,采用定量与定性相结合思路,提出了面向系统层级的复杂工业过程全息故障诊断框架,为复杂工业全流程的过程监测提供一整套技术和解决方案。相比于目前的故障诊断方法,该框架不仅包括故障检测和故障辨识,还包括故障根源诊断、故障传播路径识别、故障的定量诊断与评估,可有效解决复杂工业过程系统的综合故障诊断问题,实用性强,能够有效地减少或避免故障发生、保证产品的质量、提高企业的生产效率与生产安全;最后对故障诊断技术的发展趋势和亟待解决的问题进行了展望。     

基于独立元分析算法的过程故障检测方法

文章针对目前实际工业生产中变量不能严格服从高斯分布,且大量变量之间存有严重相关性的特点,运用ICA方法提取高维数据中独立的信号,在保留数据信息的前提下对噪声加以抑制。信号提取后分别构造监控统计量,实施过程监控和故障诊断,并利用独立元模型对CSTR仿真实时数据进行故障检测研究,仿真结果表明该方法能快速准确的检测到运行中发生的异常。     

PCA和PPCA在化工分离过程监控中的应用比较

在利用主元分析(PCA)作统计监控时,没有主元与变量之间的生成模型,出现了检测指标量度不一致且只能离线故障识别等缺陷.而概率主元分析(PPCA)则在确定主元和误差的概率函数后,利用期望最大化(EM)算法建立了过程的生成模型,克服了PCA的不足.最后通过PCA和PPCA在化工分离过程监控中的应用比较,证明PPCA监控法方便、有效.     

A novel dynamic bayesian network‐based networked process monitoring approach for fault detection,propagation identification,and root cause diagnosis

A novel networked process monitoring, fault propagation identification, and root cause diagnosis approach is developed in this study. First, process network structure is determined from prior process knowledge and analysis. The network model parameters including the conditional probability density functions of different nodes are then estimated from process operating data to characterize the causal relationships among the monitored variables. Subsequently, the Bayesian inference‐based abnormality likelihood index is proposed to detect abnormal events in chemical processes. After the process fault is detected, the novel dynamic Bayesian probability and contribution indices are further developed from the transitional probabilities of monitored variables to identify the major faulty effect variables with significant upsets. With the dynamic Bayesian contribution index, the statistical inference rules are, thus, designed to search for the fault propagation pathways from the downstream backwards to the upstream process. In this way, the ending nodes in the identified propagation pathways can be captured as the root cause variables of process faults. Meanwhile, the identified fault propagation sequence provides an in‐depth understanding as to the interactive effects of faults throughout the processes. The proposed approach is demonstrated using the illustrative continuous stirred tank reactor system and the Tennessee Eastman chemical process with the fault propagation identification results compared against those of the transfer entropy‐based monitoring method. The results show that the novel networked process monitoring and diagnosis approach can accurately detect abnormal events, identify the fault propagation pathways, and diagnose the root cause variables. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2348–2365, 2013     

注塑成型过程温度启动过渡状态的性能监测与故障诊断

注塑成型过程启动阶段具有复杂动态特性,更容易遭受未知扰动、误操作甚至故障的危害。针对注塑启动阶段的机筒温度变量,建立了二维动态多元统计模型,实现了注塑启动性能的实时监测和故障诊断;并以常见的加热线圈以及传感器故障为例,详细讨论了方法的应用效果以及在安全生产方面的应用前景。     

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。     

基于多工况混合模型的故障监控方法研究

针对多工况过程建立了一个多工况高斯混合模型(Gaussian Mixture Model,GMM),并利用EM(Expectation Maximization)算法对该GMM参数进行估计。通过引入贝叶斯阴阳算法(Bayesian YingYang,BYY),实现了GMM中混合工况数目的自动估计。然后,通过在所建GMM的每个分量中构建PCA模型,建立一个多工况故障监控混合模型。最后利用TE过程研究证明了所建模型在过程监控中的有效性。     

Operating condition diagnosis based on HMM with adaptive transition probabilities in presence of missing observations

A new approach for modeling and monitoring of the multivariate processes in presence of faulty and missing observations is introduced. It is assumed that operating modes of the process can transit to each other following a Markov chain model. Transition probabilities of the Markov chain are time varying as a function of the scheduling variable. Therefore, the transition probabilities will be able to vary adaptively according to different operating modes. In order to handle the problem of missing observations and unknown operating regimes, the expectation maximization algorithm is used to estimate the parameters. The proposed method is tested on two simulations and one industrial case studies. The industrial case study is the abnormal operating condition diagnosis in the primary separation vessel of oil‐sand processes. In comparison to the conventional methods, the proposed method shows superior performance in detection of different operating conditions of the process. © 2014 American Institute of Chemical Engineers AIChE J, 61: 477–493, 2015     

基于稀疏过滤特征学习的化工过程故障检测方法

过程安全一直以来是化学工业中尤为重要的问题之一,故障检测与诊断（FDD）作为化工异常工况管理最有力的工具之一,给过程安全提供了保障。随着深度学习的发展,很多智能学习算法已经被提出,然而这些算法却很少被应用到FDD中来。提出了一种基于稀疏过滤和逻辑回归（SFLR）算法的化工过程故障检测新方法。采用TE过程和环己烷无催化氧化制环己酮过程对提出的方法进行了验证,结果表明,所提出的方法均具有较高的诊断精度,案例研究表明提出的方法可以及时有效地诊断出故障。     

改进PCA及其在过程监测与故障诊断中的应用

提出一种改进的主元分析 (PCA)方法 ,采用主元相关变量残差 (PVR)统计量代替通常的平方预测误差Q统计量 ,用于工业过程的监测与故障诊断。改进PCA避免了Q统计量的保守性 ,能够提供更详细的过程变化信息 ,从而有效识别正常工况改变与过程故障引起的T² 图变化。通过对双效蒸发过程的仿真监测 ,与普通PCA方法进行了比较 ,表明了改进PCA方法的有效性     

基于LabVIEW与Matlab的TE过程监控系统设计

针对TE模型可视化效果不佳的问题,基于LabVIEW、Matlab与My SQL开发了TE过程仿真系统。仿真系统采用LabVIEW设计搭建了复杂、友好的系统图形界面;Simulink作为后台引擎,运用Matlab平台设计TE过程的故障监控算法,My SQL作为后台数据库存储实验结果。实现了TE过程的实时趋势显示、故障报警、历史数据记录及故障报警等功能模块。通过该仿真完成TE基本工况、内置故障、添加扰动及多工况等模式的仿真测试和故障诊断。该仿真平台已进入实验室应用阶段,为研究化工过程控制等复杂系统的故障诊断提供了良好的应用平台。     

基于相关系数的过程系统故障检测与诊断方法

故障检测和故障诊断对提高控制系统的安全性具有重要意义。通过对过程变量之间的相关性变化与过程装置故障之间关系进行深入分析,提出了一种基于过程变量相关系数约束的过程故障诊断方法。对相关过程变量定义基于相关系数（含相关系数、多重相关系数、偏相关系数）约束的过程诊断函数,通过考察相关系数和诊断函数的变化,对与其所涉及变量相关的装置是否发生故障做出判断。如果装置或系统发生故障,则会引起相关系数和诊断函数值发生变化,可通过诊断函数值进行逻辑推断,最终确定故障位置和故障装置。最后通过一个精馏塔的实际工程案例,验证了该方法的有效性。     

A support vector clustering‐based probabilistic method for unsupervised fault detection and classification of complex chemical processes using unlabeled data

A new support vector clustering (SVC)‐based probabilistic approach is developed for unsupervised chemical process monitoring and fault classification in this article. The spherical centers and radii of different clusters corresponding to normal and various kinds of faulty operations are estimated in the kernel feature space. Then the geometric distance of the monitored samples to different cluster centers and boundary support vectors are computed so that the distance–ratio–based probabilistic‐like index can be further defined. Thus, the most probable clusters can be assigned to the monitored samples for fault detection and classification. The proposed SVC monitoring approach is applied to two test scenarios in the Tennessee Eastman Chemical process and its results are compared to those of the conventional K‐nearest neighbor Fisher discriminant analysis (KNN‐FDA) and K‐nearest neighbor support vector machine (KNN‐SVM) methods. The result comparison demonstrates the superiority of the SVC‐based probabilistic approach over the traditional KNN‐FDA and KNN‐SVM methods in terms of fault detection and classification accuracies. © 2012 American Institute of Chemical Engineers AIChE J, 59: 407–419, 2013     

TeCSMART: A hierarchical framework for modeling and analyzing systemic risk in sociotechnical systems

Recent systemic failures in different domains continue to remind us of the fragility of complex sociotechnical systems. Although these failures occurred in different domains, there are common failure mechanisms that often underlie such events. Hence, it is important to study these disasters from a unifying systems engineering perspective so that one can understand the commonalities as well as the differences to prevent or mitigate future events. A new conceptual framework that systematically identifies the failure mechanisms in a sociotechnical system, across different domains is proposed. Our analysis includes multiple levels of a system, both social and technical, and identifies the potential failure modes of equipment, humans, policies, and institutions. With the aid of three major recent disasters, how this framework could help us compare systemic failures in different domains and identify the common failure mechanisms at all levels of the system is demonstrated. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3065–3084, 2016     

化工过程故障诊断研究进展

介绍化工过程故障诊断技术的理论与工业应用的现状,分析了故障诊断的过程与实质,介绍了常用的几种诊断方法,重点阐述了智能诊断的方法,现状,并对故障诊断的发展动向作了简要的分析。     

基于Logistic和ARMA模型的过程报警预测

提出了一种基于Logistic回归模型和ARMA模型相结合的过程报警事件预测方法,从历史数据中提取过程报警事件序列,并分解成报警状态及报警状态的持续时间,对应建立Logistic回归模型和ARMA模型分别对其进行预测,最终实现对过程报警事件的预测。通过数值实例分析和工业过程数据进行了验证,表明该方法能够准确地预测过程报警事件。