A computational model for evaluating the effects of attention, memory, and mental models on situation assessment of nuclear power plant operators

Operators in nuclear power plants have to acquire information from human system interfaces (HSIs) and the environment in order to create, update, and confirm their understanding of a plant state, as failures of situation assessment may cause wrong decisions for process control and finally errors of commission in nuclear power plants. A few computational models that can be used to predict and quantify the situation awareness of operators have been suggested. However, these models do not sufficiently consider human characteristics for nuclear power plant operators.In this paper, we propose a computational model for situation assessment of nuclear power plant operators using a Bayesian network. This model incorporates human factors significantly affecting operators’ situation assessment, such as attention, working memory decay, and mental model.As this proposed model provides quantitative results of situation assessment and diagnostic performance, we expect that this model can be used in the design and evaluation of human system interfaces as well as the prediction of situation awareness errors in the human reliability analysis.     

Development of the step complexity measure for emergency operating procedures using entropy concepts

For a nuclear power plant (NPP), symptom-based emergency operating procedures (EOPs) have been adopted to enhance the safety of NPPs through reduction of operators’ workload under emergency conditions. Symptom-based EOPs, however, could place a workload on operators because they have to not only identify related symptoms, but also understand the context of steps that should be carried out. Therefore, many qualitative checklists are suggested to ensure the appropriateness of steps included in EOPs. However, since these qualitative evaluations have some drawbacks, a quantitative measure that can roughly estimate the complexity of EOP steps is imperative to compensate for them.In this paper, a method to evaluate the complexity of an EOP step is developed based on entropy measures that have been used in software engineering. Based on these, step complexity (SC) measure that can evaluate SC from various viewpoints (such as the amount of information/operators’ actions included in each EOP step, and the logic structure of each EOP step) was developed.To verify the suitableness of the SC measure, estimated SC values are compared with subjective task load scores obtained from the NASA-TLX (task load index) method and step performance time obtained from a full scope simulator. From these comparisons, it was observed that estimated SC values generally agree with the NASA-TLX scores and step performance time data. Thus, it could be concluded that the developed SC measure would be considered for evaluating SC of an EOP step.     

Identification and classification of dynamic event tree scenarios via possibilistic clustering: Application to a steam generator tube rupture event

This paper illustrates a method to identify and classify scenarios generated in a dynamic event tree (DET) analysis. Identification and classification are carried out by means of an evolutionary possibilistic fuzzy C-means clustering algorithm which takes into account not only the final system states but also the timing of the events and the process evolution. An application is considered with regards to the scenarios generated following a steam generator tube rupture in a nuclear power plant. The scenarios are generated by the accident dynamic simulator (ADS), coupled to a RELAP code that simulates the thermo-hydraulic behavior of the plant and to an operators’ crew model, which simulates their cognitive and procedures-guided responses.A set of 60 scenarios has been generated by the ADS DET tool. The classification approach has grouped the 60 scenarios into 4 classes of dominant scenarios, one of which was not anticipated a priori but was “discovered” by the classifier. The proposed approach may be considered as a first effort towards the application of identification and classification approaches to scenarios post-processing for real-scale dynamic safety assessments.     

A probabilistic cognitive simulator for HRA studies (PROCOS)

The paper deals with the development of a simulator for approaching human errors in complex operational frameworks (e.g., plant commissioning). The aim is to integrate the quantification capabilities of the so-called ‘first-generation’ human reliability assessment (HRA) methods with a cognitive evaluation of the operator. The simulator allows analysing both error prevention and error recovery. It integrates cognitive human error analysis with standard hazard analysis methods (Hazop and event tree) by means of a ‘semi static approach’. The comparison between the results obtained through the proposed approach and those of a traditional HRA method such as human error assessment and reduction technique, shows the capability of the simulator to provide coherent and accurate analysis.     

An analytic model for situation assessment of nuclear power plant operators based on Bayesian inference

Simulation-based human reliability analysis (HRA) methods such as IDAC seem to provide a new direction for the development of advanced HRA methods. In such simulation-based HRA methods, the simulation model for the situation assessment of nuclear power plant (NPP) operators is essential, especially for addressing the issue of errors-of-commission (EOCs). Therefore, we propose an analytic model for the situation assessment of NPP operators based on Bayesian inference. The proposed model is found to be able to address several important features of the situation assessment of NPP operators, and is expected to provide good approximations to some parts of the situation assessment. A comparison with an existing model and identification of several other features of the situation assessment of NPP operators that should be further addressed are also provided.     

OPERA—a human performance database under simulated emergencies of nuclear power plants

In complex systems such as the nuclear and chemical industry, the importance of human performance related problems is well recognized. Thus a lot of effort has been spent on this area, and one of the main streams for unraveling human performance related problems is the execution of HRA. Unfortunately a lack of prerequisite information has been pointed out as the most critical problem in conducting HRA. From this necessity, OPERA database that can provide operators’ performance data obtained under simulated emergencies has been developed. In this study, typical operators’ performance data that are available from OPERA database are briefly explained. After that, in order to ensure the appropriateness of OPERA database, operators’ performance data from OPERA database are compared with those of other studies and real events. As a result, it is believed that operators’ performance data of OPERA database are fairly comparable to those of other studies and real events. Therefore it is meaningful to expect that OPERA database can be used as a serviceable data source for scrutinizing human performance related problems including HRA.     

Assessment of human–machine interface design for a Chinese nuclear power plant

A nuclear power plant (NPP) is a complex system but requires high reliability. The human–machine interface (HMI) design plays very important role in reactor safety. This paper describes an assessment on HMI design of a Chinese NPP, using a software system named Dynamic Interaction Analysis Support (DIAS). DIAS can give not only quantitative indices for dynamically assessing the HMI design, but also allow modify the values of these indices by taking into account human error probability during specified emergent operation procedures. The operation procedures dealing with postulated accidents and transients recorded from a full-scale plant simulator in the training center of a Chinese NPP were selected as references. According to the results of simulation and analysis, the potential problems in the HMI design and the operation procedures were detected. Suggestions to improve the HMI design and the operation procedures were addressed.     

Localized auxin peaks in concentration-based transport models of the shoot apical meristem

We study the formation of auxin peaks in a generic class of concentration-based auxin transport models, posed on static plant tissues. Using standard asymptotic analysis, we prove that, on bounded domains, auxin peaks are not formed via a Turing instability in the active transport parameter, but via simple corrections to the homogeneous steady state. When the active transport is small, the geometry of the tissue encodes the peaks’ amplitude and location: peaks arise where cells have fewer neighbours, that is, at the boundary of the domain. We test our theory and perform numerical bifurcation analysis on two models that are known to generate auxin patterns for biologically plausible parameter values. In the same parameter regimes, we find that realistic tissues are capable of generating a multitude of stationary patterns, with a variable number of auxin peaks, that can be selected by different initial conditions or by quasi-static changes in the active transport parameter. The competition between active transport and production rate determines whether peaks remain localized or cover the entire domain. In particular, changes in the auxin production that are fast with respect to the cellular life cycle affect the auxin peak distribution, switching from localized spots to fully patterned states. We relate the occurrence of localized patterns to a snaking bifurcation structure, which is known to arise in a wide variety of nonlinear media, but has not yet been reported in plant models.     

Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents. Part 4: IDAC causal model of operator problem-solving response

This is the fourth in a series of five papers describing the Information, Decision, and Action in Crew context (IDAC) operator response model for human reliability analysis. An example application of this modeling technique is also discussed in this series. The model has been developed to probabilistically predicts the responses of a nuclear power plant control room operating crew in accident conditions. The operator response spectrum includes cognitive, emotional, and physical activities during the course of an accident. This paper assesses the effects of the performance-influencing factors (PIFs) affecting the operators’ problem-solving responses including information pre-processing (I), diagnosis and decision making (D), and action execution (A). Literature support and justifications are provided for the assessment on the influences of PIFs.     

基于EMD与神经网络的滚动轴承故障诊断方法

针对滚动轴承故障振动信号的非平稳特征，提出了一种基于经验模态分解(Empirical Mode Decomposition，简称EMD)和神经网络的滚动轴承故障诊断方法。该方法首先对原始信号进行了经验模态分解，将其分解为多个平稳的固有模态函数(Intrinsic Mode function，简称IMF)之和，再选取若干个包含主要故障信息的IMF分量进行进一步分析，由于滚动轴承发生故障时，加速度振动信号各频带的能量会发生变化，因而可从各IMF分量中提取能量特征参数作为神经网络的输入参数来识别滚动轴承的故障类型。对滚动轴承的正常状态、内圈故障和外圈故障信号的分析结果表明，以EMD为预处理器提取各频带能量作为特征参数的神经网络诊断方法比以小波包分析为预处理器的神经网络诊断方法有更高的故障识别率，可以准确、有效地识别滚动轴承的工作状态和故障类型。     

RAMS+C informed decision-making with application to multi-objective optimization of technical specifications and maintenance using genetic algorithms

The role of technical specifications and maintenance (TSM) activities at nuclear power plants (NPP) aims to increase reliability, availability and maintainability (RAM) of Safety-Related Equipment, which, in turn, must yield to an improved level of plant safety. However, more resources (e.g. costs, task force, etc.) have to be assigned in above areas to achieve better scores in reliability, availability, maintainability and safety (RAMS). Current situation at NPP shows different programs implemented at the plant that aim to the improvement of particular TSM-related parameters where the decision-making process is based on the assessment of the impact of the change proposed on a subgroup of RAMS+C attributes.This paper briefly reviews the role of TSM and two main groups of improvement programs at NPP, which suggest the convenience of considering the approach proposed in this paper for the Integrated Multi-Criteria Decision-Making on changes to TSM-related parameters based on RAMS+C criteria as a whole, as it can be seem as a decision-making process more consistent with the role and synergic effects of TSM and the objectives and goals of current improvement programs at NPP. The case of application to the Emergency Diesel Generator system demonstrates the viability and significance of the proposed approach for the Multi-objective Optimization of TSM-related parameters using a Genetic Algorithm.     

A logic flowgraph-based concept for decision support and management of nuclear plant operation

The architecture of au automated decision support system for nuclear plant operators is presented and discussed. The system is based on the use of ‘logic flowgraph’ process models and is designed in a hierarchical fashion. Its functionality spans from ‘function oriented’ plant status and alternative success path information displayed to the plant operators at its higher access levels to ‘process oriented’ diagnostic and recovery information deduced and displayed at its lowest. The design basis for this architecture is the ‘defense in depth’ plant safety concept. The decision support system goal is to provide plant operators, in the presence of an unforeseen transient, with the best and safest alternative between plant stabilization after shutdown and recovery of normal operation based on early diagnosis. Examples of the system capability to interpret and diagnose abnormal plant conditions and of the information that it can supply to the operators at its three access levels are presented and discussed.     

Application of genetic algorithms to fault diagnosis in nuclear power plants

A nuclear power plant (NPP) is a complex and highly reliable special system. Without expert knowledge, fault confirmation in the NPP can be prevented by illusive and real-time signals. A new method of fault diagnosis, based on genetic algorithms (GAs) has been developed to resolve this problem. This NPP fault diagnosis method combines GAs and classical probability with an expert knowledge base. By assessing the state of the NPP, the GA colony undergoes a transformation that produces an individual adapted to the NPP's condition. Experiments performed on the 950 MW full size simulator at the Beijing NPP simulation training center show that this method has comparative adaptability to diagnose signals and faults changed with time, imperfect expert knowledge, illusive signals and other phenomena.     

An analysis of maintenance failures at a nuclear power plant

In this paper, a study of faults caused by maintenance activities is presented. The objective of the study was to draw conclusions on the unplanned effects of maintenance on nuclear power plant (NPP) safety and system availability. More than 4400 maintenance history reports from the years 1992–1994 of Olkiluoto BWR NPP were analysed together with the maintenance personnel. The human action induced faults were classified, e.g. according to their multiplicity and effects. This paper presents and discusses the results of a statistical analysis of the data. Instrumentation and electrical components appeared to be especially prone to human failures. Many human failures were found in safety related systems. Several failures also remained latent from outages to power operation. However, the safety significance of failures was generally small. Modifications were an important source of multiple human failures. Plant maintenance data is a good source of human reliability data and it should be used more in the future.     

An efficient algorithm for determining the risk of structural failure locally initiated by faults

An efficient algorithm has been proposed for determining the probability of failure of structures containing flaws. The algorithm is based on a powerful generic equation, a central parameter in which is the conditional individual probability of initiating failure by a single flaw. The equation avoids conservative predictions related to the probability of locally initiated failure and is a powerful alternative to existing approaches. It is based on the concept of ‘conditional individual probability of initiating failure’ characterising a single fault, which permits us to relate in a simple fashion the conditional individual probability of failure characterising a single fault to the probability of failure characterising a population of faults. A method for estimating the conditional individual probability has been proposed based on combining a Monte Carlo simulation and a failure criterion.The generic equation has been modified to determine the probability of fatigue failure initiated by flaws. Other important applications discussed in the paper also include: comparing different types of loading and selecting the type of loading associated with the smallest probability of over-stress failure; optimizing designs by minimizing their vulnerability to over-stress failure initiated by flaws; determining failure triggered by random faults in a large system and determining the probability of overloading of a supply system from random demands.     

A risk assessment methodology for incorporating uncertainties using fuzzy concepts

This paper proposes a new methodology for incorporating uncertainties using fuzzy concepts into conventional risk assessment frameworks. This paper also introduces new forms of fuzzy membership curves, designed to consider the uncertainty range that represents the degree of uncertainties involved in both probabilistic parameter estimates and subjective judgments, since it is often difficult or even impossible to precisely estimate the occurrence rate of an event in terms of one single crisp probability.It is to be noted that simple linguistic variables such as ‘High/Low’ and ‘Good/Bad’ have the limitations in quantifying the various risks inherent in construction projects, but only represent subjective mental cognition adequately. Therefore, in this paper, the statements that include some quantification with giving specific value or scale, such as ‘Close to any value’ or ‘Higher/Lower than analyzed value’, are used in order to get over the limitations.It may be stated that the proposed methodology will be very useful for the systematic and rational risk assessment of construction projects.     

New trends in power system planning and related effect on the reliability evaluation

ENEL, a vertically integrated utility for generation, transmission and distribution in Italy, has been a worldwide leader since the 1970s in setting up ‘home made’ computing programs for the adequacy evaluation, imbedded in the majority of studies needed in power system planning and operation, of large composite generation and transmission systems.Adequacy evaluation has a probabilistic content. Two complementary approaches have been adopted at ENEL, direct probabilistic approach and Monte Carlo simulation, implementing a number of computing programs tailored to the Italian situation. Naturally, those approaches have been complemented by deterministic evaluations, with the well-known computer programs used in power system planning and operation.The recent changes in the electric supply industry, ‘liberalized’ and privatized at different extent in the various countries, emphasized the importance of the reliability evaluation as a key issue for the establishment of revised/updated adequacy/security standards aiming to the proper allocations of the investments. In Italy the major ‘new’ issues are presently the effect/compatibility of the non-utility generators with ENEL mixed hydrothermal generation system and the increasing ‘size’ of the transmission network, evermore interconnected with other foreign systems, which ask for suitable ‘adequacy-orientated’ equivalents and ‘new’ tools for the evaluation of concept such as ‘transmission duty pricing’.The paper describes how at ENEL the new trends are faced and the work which ENEL, now a joint stock company which—in addition to the obligation to supply—has new responsibilities against the potential stockholders, has under way in the field of adequacy assessment.     

The effect of two complexity factors on the performance of emergency tasks—An experimental verification

It is well known that the use of procedures is very important in securing the safety of process systems, since good procedures effectively guide human operators by providing ‘what should be done’ and ‘how to do it’, especially under stressful conditions. At the same time, it has been emphasized that the use of complicated procedures could drastically impair operators’ performance. This means that a systematic approach that can properly evaluate the complexity of procedures is indispensable for minimizing the side effects of complicated procedures. For this reason, Park et al. have developed a task complexity measure called TACOM that can be used to quantify the complexity of tasks stipulated in emergency operating procedures (EOPs) of nuclear power plants (NPPs). The TACOM measure consists of five sub-measures that can cover five important factors making the performance of emergency tasks complicated. However, a verification activity for two kinds of complexity factors—the level of abstraction hierarchy (AH) and engineering decision (ED)—seems to be insufficient. In this study, therefore, an experiment is conducted by using a low-fidelity simulator in order to clarify the appropriateness of these complexity factors. As a result, it seems that subjects’ performance data are affected by the level of AH as well as ED. Therefore it is anticipate that both the level of AH and ED will play an important role in evaluating the complexity of EOPs.