定性代数和图论方法用于连续系统动态定性趋势分析

Qualitative algebraic equations are the basis of qualitative simulation,which are used to express the dynamic behavior of steady-state continuous processes.When the values and operation of qualitative variables are redefined,qualitative algebraic equations can be transformed into signed direct graphs,which are frequently used to predict the trend of dynamic changes.However,it is difficult to use traditional qualitative algebra methods based on artificial trial and error to solve a complex problem for dynamic trends.An important aspect of modern qualitative algebra is to model and characterize complex systems with the corresponding computer-aided automatic reasoning.In this study,a qualitative affection equation based on multiple conditions is proposed,which enables the signed di-rect graphs to describe complex systems better and improves the fault diagnosis resolution.The application to an industrial case shows that the method performs well.     

基于非线性主元分析和符号有向图的故障诊断方法

Nonlinear principal component analysis（NLPCA）fault detection method achieves good detection results especially in a nonlinear process.Signed directed graph（SDG）model is based on deep-going information,which excels in fault interpretation.In this work,an NLPCA-SDG fault diagnosis method was proposed.SDG model was used to interpret the residual contributions produced by NLPCA.This method could overcome the shortcomings of traditional principal component analysis（PCA）method in fault detection of a nonlinear process and the shortcomings of traditional SDG method in single variable statistics in discriminating node conditions and threshold values.The application to a distillation unit of a petrochemical plant illustrated its validity in nonlinear process fault diagnosis.     

Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes

This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is in-troduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to re-trieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.     

Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map （SOM） with Fisher discriminant analysis （FDA） is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman （TE） process is em- ployed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.     

基于Fisher判别分析和核回归的质量监控和估计

A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is under normal condition, then kernel regression is further used for quality prediction and estimation. If faults have occurred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can effectively detect the fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.     

A local and global statistics pattern analysis method and its application to process fault identification☆

Traditional principal component analysis (PCA) is a second-order method and lacks the ability to provide higher-order representations for data variables. Recently, a statistics pattern analysis (SPA) framework has been incor-porated into PCA model to make full use of various statistics of data variables effectively. However, these methods omit the local information, which is also important for process monitoring and fault diagnosis. In this paper, a local and global statistics pattern analysis (LGSPA) method, which integrates SPA framework and locality pre-serving projections within the PCA, is proposed to utilize various statistics and preserve both local and global in-formation in the observed data. For the purpose of fault detection, two monitoring indices are constructed based on the LGSPA model. In order to identify fault variables, an improved reconstruction based contribution (IRBC) plot based on LGSPA model is proposed to locate fault variables. The RBC of various statistics of original process variables to the monitoring indices is calculated with the proposed RBC method. Based on the calculated RBC of process variables' statistics, a new contribution of process variables is built to locate fault variables. The simula-tion results on a simple six-variable system and a continuous stirred tank reactor system demonstrate that the proposed fault diagnosis method can effectively detect fault and distinguish the fault variables from normal variables.     

High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process☆

Purified terephthalic acid (PTA) is an important chemical raw material. P-xylene (PX) is transformed to terephthalic acid (TA) through oxidation process and TA is refined to produce PTA. The PX oxidation reaction is a complex process involving three-phase reaction of gas, liquid and solid. To monitor the process and to im-prove the product quality, as wel as to visualize the fault type clearly, a fault diagnosis method based on self-organizing map (SOM) and high dimensional feature extraction method, local tangent space alignment (LTSA), is proposed. In this method, LTSA can reduce the dimension and keep the topology information simultaneously, and SOM distinguishes various states on the output map. Monitoring results of PX oxidation reaction process in-dicate that the LTSA–SOM can wel detect and visualize the fault type.     

基于高阶统计和解释结构模型相结合的大型过程控制系统的故障检测(英文)

Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical (HOS) is an effec-tive data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model (ISM) and HOS is proposed:(1) the adja-cency matrix is determined by partial correlation coefficient;(2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram;(3) interpretative structural for large-scale process control system is built by this ISM method;and (4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamical y based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.     

Accelerated Recursive Feature Elimination Based on Support Vector Machine for Key Variable Identification

Key variable identification for classifications is related to many trouble-shooting problems in process industries. Recursive feature elimination based on support vector machine （SVM-RFE） has been proposed recently in application for feature selection in cancer diagnosis. In this paper, SVM-RFE is used to the key variable selection in fault diagnosis, and an accelerated SVM-RFE procedure based on heuristic criterion is proposed. The data from Tennessee Eastman process （TEP） simulator is used to evaluate the effectiveness of the key variable selection using accelerated SVM-RFE （A-SVM-RFE）. A-SVM-RFE integrates computational rate and algorithm effectiveness into a consistent framework. It not only can correctly identify the key variables, but also has very good computational rate. In comparison with contribution charts combined with principal component aralysis （PCA） and other two SVM-RFE algorithms, A-SVM-RFE performs better. It is more fitting for industrial application.     

基于GAMs和Bootstrap的生化过程建模和故障监控(英文)

Fault monitoring of bioprocess is important to ensure safety of a reactor and maintain high quality of products. It is difficult to build an accurate mechanistic model for a bioprocess, so fault monitoring based on rich historical or online database is an effective way. A group of data based on bootstrap method could be resampling stochastically, improving generalization capability of model. In this paper, online fault monitoring of generalized additive models (GAMs) combining with bootstrap is proposed for glutamate fermentation process. GAMs and bootstrap are first used to decide confidence interval based on the online and off-line normal sampled data from glutamate fermentation experiments. Then GAMs are used to online fault monitoring for time, dissolved oxygen, oxygen uptake rate, and carbon dioxide evolution rate. The method can provide accurate fault alarm online and is helpful to provide useful information for removing fault and abnormal phenomena in the fermentation.     

基于SDG模型的控制系统故障诊断方法

利用深层定性知识模型、符号有向图模型,对生产流程中常见的单回路和串级控制系统进行了建模与故障诊断研究。提出基于假设-验证的双向推理算法,解决了传统的故障诊断方法从未涉及复杂系统中的多个控制回路对系统故障存在的屏蔽作用的问题。通过对某厂常压蒸馏装置进行基于SDG模型的故障诊断,验证了基于SDG模型的故障诊断方法应用在包含多个控制回路的复杂系统故障诊断时的有效性与可行性。     

基于知识故障诊断系统所用的深层知识及SDG方法

基于知识的故障诊断系统不需要对象的精确数学模型,具有很强的生命力．其诊断能力与知识库中知识的深浅、容量和精度成正向关系。讨论了基于知识的故障诊断系统所用的各种深层知识,并认为：系统的深层知识是基于系统结构的功能模型和行为模型,它们既可以用定性方式,也可以用定量方式描述。SDG（符号定向图）方法基于深层知识模型进行推理,是一种完备的揭示系统潜在故障的有效方法。     

一类连续反应的SDG HAZOP与故障诊断

在分析苻号有向图（SDG）技术的理论基础上,探讨了SDG定性模型在计算机辅助HAZOP（危险与可操作性分析）和故障诊断中的实际应用,并针对该丙烯聚合反应（溶剂淤浆法）建立SDG模型,利用SDG技术进行HAZOP和故障诊断分析。实验结果表明,基于SDG的HAZOP和故障诊断技术具有完备性好、节省时间、人力、费用等众多优点。     

基于定性趋势与符号有向图的模型校核方法

模型校核的首要目标是建立一个完备的测试剧情集。针对现有模型校核方法存在的问题,提出了基于定性趋势与符号有向图的模型校核方法。首先,在总结前人多种SDG建模方法的基础上,提出了SDG校核模型的建模方法。其次,提出了基于定性趋势与符号有向图的模型校核方法,包括建立SDG校核模型、产生测试剧情、推理标准趋势序列、仿真模型数据趋势提取与识别和趋势对比分析5部分。最后,以TE模型为例进行分析,证明该方法的有效性。     

A new qualitative trend analysis algorithm based on global polynomial fit

Qualitative trend analysis (QTA) is an effective tool for process data analysis, the applications of which can be found in a variety of fields, such as process monitoring, fault diagnosis, and data mining. Reliable and accurate trend extraction of sensor data is the first and indispensable step in QTA. In this article, a new trend extraction algorithm is developed that is based on global optimization of the polynomial fit of the process data. Different from most existing works, this newly proposed algorithm solves the trend extraction task by simultaneously and globally estimating the episode number, the boundary time points of the episode, and the fitted polynomial coefficients, which shows improved performance over other nonglobally optimal trend extraction algorithms and requires less a priori knowledge than the existing globally optimal trend algorithms. The effectiveness of the algorithm is illustrated by testing on a variety of simulation and real blast furnace data. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3374–3383, 2017     

基于多块信息提取的PCA故障诊断方法

传统的监控方法往往只利用传感器观测值信息进行过程的故障监测，而忽略了原始数据中包含的其他有效信息。为此，提出一种基于多块信息提取的PCA故障监测算法。首先，对过程变量的累计误差和变化率信息进行定义，从而能够从数据中提取新的特征信息，并基于每种特征将过程划分为3个子块；然后，利用PCA方法对每个子块进行建模与监测，通过贝叶斯方法对监测结果进行融合；最后，提出一种基于加权贡献图的故障诊断方法，分离出引发故障的源变量。通过数值例子与田纳西-伊斯曼（TE）过程监控中的应用证明了所提方法的有效性与可行性。     

层次SDG方法在脱硫系统故障诊断中的应用

针对纯定性的SDG模型在复杂系统中建模难度大、分辨率低、可能产生信息爆炸的问题,提出一种结合层次分析法的结构递阶层次,划分故障并建立层次SDG图的方法.在实际应用中结合密相干塔烟气脱硫系统,建立了层次SDG分析,提高了诊断速度.     

符号有向图在化工安全评价中的应用进展

符号有向图（Signed Directed Graph,SDG）是一种定性描述过程系统故障与故障源之间因果关系的模型方法。对SDG的研究进展进行了全面总结,通过实例介绍了SDG定性模型,重点介绍了SDG半定量模型以及SDG在安全评价中的研究成果,最后对未来发展趋势进行了展望。