大规模复杂系统的定性SDG建模方法

介绍定性符号有向图（SDG）的建模思想--通过数学模型建立关联矩阵,再转化为图表示,并进行化简.总结了三条化简规则,阐述了如何通过实验,即灵敏度分析来形成关联矩阵的方法.在此基础上总结了定性SDG的建模步骤,并结合65t/h锅炉系统的典型实例,研究建立其定性SDG模型问题.     

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。     

用于化工过程的SDG故障分析方法

基于符号有向图（signed directed graph,SDG）的化工过程模型可以表征过程故障与故障源之间的因果关系,SDG故障分析方法通过有向图定性推理技术实现故障诊断。本文简要介绍了SDG故障分析的基本概念,并对Iri算法^[1]中故障源的搜索步骤提出了部分改进,并进行了实例验证。最后,利用SDG故障诊断方法对某化工厂的氯气泄漏事故的故障传播进行了分析研究。     

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。     

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。     

基于复杂网络的化工过程层次符号有向图模型建立及关键节点识别

针对定性符号有向图（signed directed graph,SDG）在化工过程系统中建模复杂度高、故障分辨率低、容易忽略部分变量等问题,提出一种基于复杂网络理论构建层次SDG网络模型并识别关键节点的方法。首先利用层次分析法对化工过程系统划分递阶层次结构,建立基于子系统的系统SDG网络模型,选取度中心性、接近中心性等多个节点重要性评价指标,采用主成分分析法确定各指标权重并利用逼近理想排序法（technique for order preference by similarity to an ideal solution,TOPSIS）多属性决策方法得到节点重要性的综合评价值,初步识别关键节点所在的子系统;然后建立子系统的SDG模型并细化为有向网络,采用LeaderRank算法对节点重要性进行排序,进而在子系统网络模型中确定关键节点的位置。案例计算结果表明该方法可以有效地降低建模的复杂性,提高关键节点识别的全面性和准确性,从而改善化工过程系统的安全稳定性。     

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.     

基于开源组件的SDG推理平台

符号有向图(SDG)是定性研究化工过程中变量间关系的有力工具,利用SDG的正向反向推理机制可以快速发掘出系统中变量之间影响的因果关系,不仅可以用于在线的故障诊断,还可以用于离线的危险和可操作性(HAZOP)分析。近年来,随着计算机和信息技术的发展,人们利用GensymG2或者C++开发了一些针对特定研究体系的SDG模型的计算机辅助推理系统,但是,目前还没有一个通用的SDG推理平台。为促进SDG在科研和教学中的广泛应用,本文介绍了通过公共组件来搭建的一套开放体系的SDG推理平台系统,这是服务器/浏览器结构的推理平台,使用方便,维护便捷。使用这套系统,可以方便地绘制特定研究体系的SDG模型的图形;可以快速建立针对特定研究体系的SDG系统并且进行相应的推理分析。本文通过一个例子演示了如何在SDG推理平台上建立特定SDG推理系统的方法,之后通过对两个研究体系建立SDG推理系统来展现该平台的有效性。     

基于多层优化PCC-SDG方法的化工过程故障诊断

化工过程的故障发生往往都是一个变量带动多个变量的连锁效应。本文基于变量的相关性变化特点,用符号有向图SDG（signed directed graph）描述系统因果影响关系,以皮尔逊相关系数PCC（Pearson correlation coefficient）计算网络统计指标,提出了一种基于多层优化PCC-SDG的故障诊断方法。该方法基于全工艺的网络拓扑结构,首先对选取的变量进行初步优化。然后,为有效提取工艺特征信息,运用PCA（principal component analysis）权重思想从多层相关系数集中选取了权重较大的关键变量,结合SDG建立最优PCC-SDG网络。最后,针对最优PCC-SDG网络变量的相关性规律重构聚集权重系数Q,进行过程故障检测与诊断。TE（Tennessee Eastman）仿真过程的应用结果表明,PCC-SDG建模及故障诊断步骤较为简洁,可以充分挖掘SDG深层次关联特性,定量简化SDG的故障诊断效果明显,具有较好的过程监控优势。     

基于图模型的间歇过程误操作风险辨识

提出了一套基于图模型的建模方法,该模型以Petri网和符号有向图（SDG）为基础将模型分为上部和下部,加入操作点、控制库所、判断模块等模型元素,并结合间歇过程的特点,提出了关联变量、目标变量检查表、操作点检查表和关联变量检查表等概念。根据误开误关、过早过晚、步骤添加删除等误操作,对模型进行验证。验证结果表明,该模型解决了先前方法难以描述具体操作的缺陷,并且模型结构简单,具有整体性。不仅能够方便地描述3种不同类型的误操作,还能有效地对误操作进行风险辨识,具有一定的适用性。     

基于符号有向图与定性趋势分析的化工过程故障诊断方法(英文)

In the past 30 years, signed directed graph (SDG), one of the qualitative simulation technologies, has been widely applied for chemical fault diagnosis. However, SDG based fault diagnosis, as any other qualitative method, has poor diagnostic resolution. In this paper, a new method that combines SDG with qualitative trend analysis (QTA) is presented to improve the resolution. In the method, a bidirectional inference algorithm based on assumption and verification is used to find all the possible fault causes and their corresponding consistent paths in the SDG model. Then an improved QTA algorithm is used to extract and analyze the trends of nodes on the consistent paths found in the previous step. New consistency rules based on qualitative trends are used to find the real causes from the candidate causes. The resolution can be improved. This method combines the completeness feature of SDG with the good diagnostic resolution feature of QTA. The implementation of SDG-QTA based fault diagnosis is done using the integrated SDG modeling, inference and post-processing software platform. Its application is illustrated on an atmospheric distillation tower unit of a simulation platform. The result shows its good applicability and efficiency.     

间歇过程动态SDG建模

目前用于间歇过程的SDG建模方式不能达到计算机辅助HAZOP的要求,本文在现有SDG-HAZOP理论基础上提出了面对间歇过程的动态SDG建模理论,并详细阐述了如何运用SDG对间歇过程进行建模的思路和方法,并运用该方法对一个煤制油流程的除灰工序进行建模和分析,同时解决了间歇过程SDG建模的模型接续性与故障和危险覆盖面最大化两方面问题。     

SDG-based Model Validation in Chemical Process Simulation

Signed direct graph (SDG) theory provides algorithms and methods that can be applied directly to chemical process modeling and analysis to validate simulation models, and is a basis for the development...     

A signed directed graph-based systematic framework for steady-state malfunction diagnosis inside control loops

Although signed directed graphs (SDG) have been widely used for modeling control loops, due to lack of adequate understanding of SDG-based steady-state process modeling, special and cumbersome methods are used to analyze control loops. In this paper, we discuss a unified SDG model for control loops, in which both disturbances (sensor bias, etc.) as well as structural faults (sensor failure, controller failure, etc.) can be easily modeled under steady-state conditions. Various fault scenarios such as external disturbances, sensor bias, controller failure, etc. have been thoroughly analyzed. A new algorithm for steady-state fault diagnosis using the SDG model for the steady-state system, that uses a combination of forward- and backward-reasoning, is proposed. Three case studies are presented to show the utility of the steady-state SDG model for fault diagnosis. A tank-level control system is used as the first case study. The second case study deals with fault diagnosis of a multi-stream-controlled CSTR. The third case study deals with fault/failure diagnosis in a process flowsheet containing a CSTR with one control loop and a flash vaporizer with three control loops.     

智能化风险及可操作性（HAZOP）分析系统研究进展

通过梳理智能化风险及可操作性（HAZOP）分析的各类方法,将其分为定性分析和定量分析两大领域,详细介绍了不同的研究方法及其优缺点,并结合实例对有代表性的方法作了详细阐述。智能化HAZOP定性分析系统的研究主要集中在建立专家知识库的方法上,其中基于深层知识建立专家知识库的研究相对较多,该方法虽然能够揭示偏差产生的机理、传递路径,但是分析结果准确度较差。基于经验知识、定性分析与定量分析相结合及基于动态模拟的HAZOP定量分析是实现智能化HAZOP定量分析的3种主要方法,是由模糊定量向精确定量、由静态分析向动态分析发展的过程。基于动态模拟的智能化HAZOP定量分析不仅研究偏差的数值,而且研究偏差的持续时间对过程系统的影响,分析结果更加精确,是较为完善的定量分析方法。该方法借助于商用模拟软件,使不具备丰富经验的分析人员也可以完成精确的HAZOP分析。"动态偏差的阶梯化求解法"使分析动态偏差方便、易行,基于该方法建立的偏差"层级"模型充分说明了处于不同层级的偏差对过程系统的不同影响。在分析智能化HAZOP分析系统发展历程基础上,结合最新研究进展,预测了其发展趋势。     

Shape anomaly detection for process monitoring of a sequencing batch reactor

Anomaly detection is critical to process modeling, monitoring, and control since successful execution of these engineering tasks depends on access to validated data. Classical methods for data validation are quantitative in nature and require either accurate process knowledge, large representative data sets, or both. In contrast, a small section of the fault diagnosis literature has focused on qualitative data and model representations. The major benefit of such methods is that imprecise but reliable results can be obtained under previously unseen process conditions. This work continues with a line of work focused on qualitative trend analysis which is the qualitative approach to data series analysis. An existing method based on shape-constrained spline function fitting is expanded to deal explicitly with discontinuities and is applied here for the first time for anomaly detection. An experimental test case and a comparison with the principal component analysis method bear out the benefits of the qualitative approach to process monitoring.