基于多层流模型的一回路报警元件分析算法研究

针对一回路发生故障时报警元件多、信息量大的特点,本文采用了基于多层流模型的方法来分析报警元件,并对警报算法进行了分析。该算法采用因果图原理对报警部件进行深度优先搜索,确定故障报警元件及报警传播路径。基于多层流模型的报警分析系统可作为运行支持系统的子系统来辅助运行人员决策。     

基于多层流模型的二回路系统故障诊断研究

通过分析核动力装置的二回路系统,并结合多层流模型(MFM)原理,建立了二回路系统的MFM.利用Visual C++,结合MFM推理规则及因果图(CDG)次序分析法建立了一套测试性的故障诊断系统,并在模拟器上进行仿真试验.结果表明,该诊断系统在故障发生时能准确判定潜在源故障,具有可视化程度高及可理解性强的优点.     

基于PCA与SDG的反应堆一回路系统故障诊断方法研究

反应堆一回路系统复杂,运行参数耦合多变,安全问题突出。为了保障运行安全、快速定位故障源,提出基于主元分析（PCA）与符号有向图（SDG）的一回路系统故障诊断模型。以一回路系统为诊断研究对象建立PCA-SDG模型,通过PCA分析监测参数的残差,判断故障的发生;然后采用SDG模型进行反向推理,找到潜在故障类型。通过模拟机仿真试验验证,该方法能够有效诊断故障,并提供报警传递路径。该方法可用于运行人员辅助决策,对运行装置的状态监测、报警分析和故障诊断具有重要意义。      

核动力装置混合智能故障诊断系统研究

针对核动力装置的状态监测与故障诊断问题,为克服单一故障诊断方法的局限性,本文提出了基于多层流模型(MFM)和信息融合理论的混合智能故障诊断方法。利用信息融合的方法来提高故障诊断的快速性和准确性,同时利用MFM方法解释故障发生时报警的传播路径,提高对诊断结果的可理解性。仿真实验表明,混合智能诊断方法能迅速判明故障并提交报警分析。     

基于MFM和SDG的二回路系统报警分析研究

将多层流模型（MFM）和符号有向图(SDG)相结合,对核动力装置二回路系统的报警信号进行分析。利用MFM对核动力装置进行抽象、分层,运用SDG的理论对MFM中的设备符号进行逻辑分析。对二回路系统的两种故障状态进行报警仿真分析,结果表明：两种方法的结合能准确地判断源故障,清晰显示报警信号传递路径,有助于操纵员进行判断。     

基于和睦系统的ACPR1000核电厂反应堆保护系统自诊断方案设计

给出了一套基于和睦系统的ACPR1000核电厂反应堆保护系统自诊断功能的设计原则,在此基础上进行了方案设计,包括和睦系统平台故障诊断、故障处理、诊断信息上报和报警指示。实测验证表明,基于该自诊断方案设计的ACPR1000核电厂反应堆保护系统自诊断功能,可以将和睦系统所有的可诊断故障进行处理、上报和指示,为ACPR1000核电厂反应堆保护系统的日常维护和故障应急处理提供足够的决策信息,满足核电领域规范和现场应用要求。     

数字化仪控系统软件可靠性定量评估研究

提出利用多层流模型对核安全级数字化仪控系统软件进行建模和可靠性定量评估的方法,开发多层流模型图形化建模和分析平台,建立某数字化仪控系统比例积分微分(PID)控制软件的多层流模型并根据测试结果进行了软件可靠性估计。所提方法通过一次分析可获得软件总目标及所有子目标的可靠度,便于识别软件设计中的薄弱环节,模型容易建立和修改。     

基于入侵性野草算法的核动力装置故障诊断

针对船用核动力装置故障原因与相应故障征兆之间并非完全一一对应的特点,提出了一种将入侵性野草算法和概率因果模型相结合的故障诊断方法,该方法将概率因果模型中的似然函数作为入侵性野草算法的适应函数,从而将复杂系统的故障诊断转化为优化问题。结果表明,该方法能用于诊断过程中出现的不确定性问题,也可实现通过多个征兆来诊断多个故障的目的,且具有较高的诊断可靠性与实用性。     

基于多层流模型和故障树的可靠性分析方法研究

多层流模型(MFM)和故障树以不同的形式描述系统知识,在相同的系统边界条件和假设下,两者表达的系统可靠性逻辑是等效的。本文工作以此为基础,结合MFM的特点,提出了MFM转换为故障树的方法,为快速建立故障树提供了一种途径,实现了基于MFM的可靠性定性分析,并以压水堆核电厂的安全注入系统为例建立了系统的MFM,定性地分析了系统的可靠性。分析结果表明,MFM转换为故障树的逻辑是正确的,且MFM易于理解、建立和修改,相对于传统建故障树的方法,大幅减少了分析人员的工作量,节省了建模时间。     

SDG故障诊断方法在核动力装置中的应用研究

核动力装置运行状态的诊断关系到装置运行的安全性和可靠性。针对核动力装置系统复杂,难以建立数学模型的特点,本文将基于定性模型的符号有向图（SDG）方法应用于核动力装置中进行故障诊断,并以核动力装置主冷却剂系统为研究对象,提出了相应的建模方法并建立了SDG模型,根据所建立的模型开发了基于SDG方法的核动力装置故障诊断系统,并以蒸汽发生器传热管破裂（SGTR）事故和弹棒事故为例对该系统的诊断推理过程进行了分析。仿真结果表明,基于SDG的方法在核动力装置中能有效诊断故障,并能提供故障传播路径,具有良好的解释性,可为运行人员决策提供帮助。     

Study of visualized simulation and analysis of nuclear fuel cycle system based on multilevel flow model

Complex energy and environment system, especially nuclear fuel cycle system recently raised social concerns about the issues of economic competitiveness, environmental effect and nuclear proliferation. Only under the condition that those conflicting issues are gotten a consensus between stakeholders with different knowledge background, can nuclear power industry be continuingly developed. In this paper, a new analysis platform has been developed to help stakeholders to recognize and analyze various socio-technical issues in the nuclear fuel cycle system based on the functional modeling method named Multilevel Flow Models （MFM） according to the cognition theory of human being. Its character is that MFM models define a set of mass, energy and information flow structures on multiple levels of abstraction to describe the functional structure of a process system and its graphical symbol repre- sentation and the means-end and part-whole hierarchical flow structure to make the represented process easy to be understood. Based upon this methodology, a micro-process and a macro-process of nuclear fuel cycle system were selected to be simulated and some analysis processes such as economics analysis, environmental analysis and energy balance analysis related to those flows were also integrated to help stakeholders to understand the process of decision-making with the introduction of some new functions for the improved Multilevel Flow Models Studio, and finally the simple simulation such as spent fuel management process simulation and money flow of nuclear fuel cycle and its levelised cost analysis will be represented as feasible examples.     

Development strategies of an expert system for multiple alarmprocessing and diagnosis in nuclear power plants

The development strategies of a prototype expert system, called ESAPD, for multiple alarm processing and diagnosis in nuclear power plants are described. The main objectives of the system are to assist operators in identifying a primary causal alarm among multiple fired alarms and to diagnose the plant malfunction quickly. The overall plant-wide diagnosis is performed at the alarm processing stage which can identify a primary causal alarm and can diagnose possible failure modes and failed systems and automatic interlock actions. The knowledge base for the alarm processing is represented as object-oriented concepts. The specific root cause diagnosis for the primary causal alarm can be performed at the alarm diagnosis stage. The system can provide operators with the possible causes of the primary causal alarm, emergency actions, and follow-up treatments. The diagnostic method adopted in this system is a hypothesize-and-test paradigm     

Development of the effectiveness measure for an advanced alarmsystem using signal detection theory

Since many alarms which are activated during major process deviations or accidents in nuclear power plants can result in negative effects for operators, various types of advanced alarm systems that can select important alarms for the identification of process deviation have been developed to reduce the operator's workload. However, the irrelevant selection of important alarms could distract the operator from correct identification of process deviation. Therefore, to evaluate the effectiveness of the advanced alarm system, a tradeoff between the alarm reduction rate (how many alarms are reduced?) and informativeness (how many important alarms that are conducive to identifying process deviation are provided?) of an advanced alarm system should be considered. In this paper, a new measure is proposed to evaluate the effectiveness of an advanced alarm system with regard to the identification of process deviation. Here, the effectiveness measure is the combination of informativeness measure and reduction rate, and the informativeness measure means the information processing capability performed by the advanced alarm system including wrong rejection and wrong acceptance, and it can be calculated using the signal detection theory (SDT). The effectiveness of the prototype alarm system was evaluated using the loss of coolant accident (LOCA) scenario, and the validity of the effectiveness measure was investigated from two types of the operator response, such as the identification accuracy and the operator's preference for the identification of LOCA. From the investigation, it can be concluded that the effectiveness measure is appropriately used to evaluate an advanced alarm system because the operator can perform the identification of process deviation more correctly and easily when the effectiveness score is sufficiently high     

Techniques to derive additional information of operation actions for computer-based operating procedure

Most of the modern main control rooms of nuclear power plants are equipped with computer-based operating procedures (CBPs), which make it easier for operators to operate and control the reactor compared with paper-based operating procedure (PBP). However, most of the CBPs do not provide necessary information which is useful for operators, especially in an emergency situation. In addition, proper decisions and actions of the procedure steps are needed to prevent human errors in mitigating the accident. The additional information, which is the information of the impact of a counter action (by automatic system and human actions) such as the components influenced and future plant behavior will be very helpful for operators to understand the effects of the counter action. The aim of the study is to develop techniques to generate the additional information. Multilevel flow modeling (MFM) is applied to model a nuclear power plant and the counter actions described in CBPs. A simple emergency operating procedure of steam generator tube rupture accident of a pressurized water reactor (PWR) plant is used as a case study. The algorithm to generate the additional information is based on the influence propagation rules and cause–effect relations expressed in the MFM model of the PWR plant.     

Development of task analysis method for operator tasks in main control room of an advanced nuclear power plant

Task analysis methods provide an insight for quantitative and qualitative predictions of how people will use a proposed system, though the different versions have different emphases. Most of the methods can attest to the coverage of the functionality of a system and all provide estimates of task performance time. However, most of the tasks that operators deal with in a digital work environment in the main control room of an advanced nuclear power plant require high mental activity. Such mental tasks overlap and must be dealt with at the same time; most of them can be assumed to be highly parallel in nature. Therefore, the primary aim to be addressed in this paper was to develop a method that adopts CPM-GOMS (cognitive perceptual motor-goals operators methods selection rules) as the basic pattern of mental task analysis for the advanced main control room. A within-subjects experiment design was used to examine the validity of the modified CPM-GOMS. Thirty participants participated in two task types, which included high- and low-compatibility types. The results indicated that the performance was significantly higher on the high-compatibility task type than on the low-compatibility task type; that is, the modified CPM-GOMS could distinguish the difference between high- and low-compatibility mental tasks.     

基于计算机模拟技术的人机界面评价系统

介绍了一种基于计算机模拟技术的核电厂控制室人机界面软件评价系统DIAS。该系统利用计算机来模拟操纵员对控制室人机界面的操作过程．给出操作过程的定量评价结果，同时它还采用人因失误预测技术(THERP)对操纵员操作失误概率进行分析。DIAS系统可以为核电厂主控室人机界面的设计及改进提供很好的技术支持。     

A cascade intelligent fault diagnostic technique for nuclear power plants

Safe operation of nuclear power plant is one of the most important tasks in nuclear power development. This justifies the variety of methods that have been proposed to support the operators in the task of plant condition monitoring, fault detection, and diagnosis. A number of hybrid fault detection and diagnosis methods have also been proposed, with their attendant weaknesses. This work proposes the hybrid of principal component analysis (PCA), signed directed graph (SDG), and Elman Neural Network (ENN) for fault detection, fault isolation, and severity estimation, respectively. The proposed hybrid method is verified with the data derived from Personal Computer Transient Analyzer (PCTRAN) simulation. The verification result shows that the PCA-based fault detection methodology realized timely detection of anomaly in the simulated nuclear power plants system, the SDG-based fault recognition method was able to isolate the system abnormality and identify the root causes, and the ENN-based fault severity estimation method presents the failure fraction of fault, representing the severity. With this integrated hybrid method, more fault information is provided for the operators, which serves as a good foundation for further decision-making and interventions.