An integrated framework to the predictive error analysis in emergency situation

This paper introduces an analysis framework and procedure to predictively analyze human errors in performing emergency tasks, which are mostly composed of cognitive activities, in nuclear power plants. The framework focuses on the cognitive errors and provides a new perspective in the utilization of context factors into cognitive error prediction. The basic viewpoint on the occurrence of cognitive error taken in this paper is that the cognitive function failures occur from the mismatch between operator's cognitive capability and the requirements of a given task and situational condition. In accordance with this viewpoint, performance influencing factors that influence the occurrence of human errors are classified into three groups, i.e. Performance Assisting Factors (PAF), Task Characteristic Factors (TCF), and Situational Factors (SF). This classification helps analysts view the overall task context in an integrative way by considering the level of PAF with the requirements of TCF and SF to predict the possibility of cognitive function failures. Further, it enables analysts to draw specific error reduction strategies. The framework suggested was applied to the analysis of cognitive error potential for the bleed and feed operation of emergency tasks in nuclear power plants.     

A systems perspective of managing error recovery and tactical re-planning of operating teams in safety critical domains

Introduction

Research in human error has provided useful tools for designing procedures, training, and intelligent interfaces that trap errors at an early stage. However, this “error prevention” policy may not be entirely successful because human errors will inevitably occur. This requires that the error management process (e.g., detection, diagnosis and correction) must also be supported. Research has focused almost exclusively on error detection; little is known about error recovery, especially in the context of safety critical systems. The aim of this paper is to develop a research framework that integrates error recovery strategies employed by experienced practitioners in handling their own errors.

Method and Results

A control theoretic model of human performance was used to integrate error recovery strategies assembled from reviews of the literature, analyses of near misses from aviation and command & control domains, and observations of abnormal situations training at air traffic control facilities. The method of system dynamics has been used to analyze and compare error recovery strategies in terms of patterns of interaction, system affordances, and types of recovery plans. System dynamics offer a promising basis for studying the nature of error recovery management in the context of team interactions and system characteristics.

Impact on industry

The proposed taxonomy of error recovery strategies can help human factors and safety experts to develop resilient system designs and training solutions for managing human errors in unforeseen situations; it may also help incident investigators to explore why people's actions and assessments were not corrected at the time.     

Classification of errors contributing to rail incidents and accidents: A comparison of two human error identification techniques

Identifying the errors that frequently result in the occurrence of rail incidents and accidents can lead to the development of appropriate prevention and/or mitigation strategies. Nineteen rail safety investigation reports were reviewed and two error identification tools, the Human factors analysis and classification system (HFACS) and the Technique for the retrospective and predictive analysis of cognitive errors (TRACEr-rail version), used as the means of identifying and classifying train driver errors associated with rail accidents/incidents in Australia. We aimed to identify the similarities and differences between the techniques in their capacity to identify and classify errors and also to determine how consistently the tools are applied. The HFACS analysis indicated that slips of attention (i.e. ‘skilled based errors’) were the most common ‘unsafe acts’ committed by drivers. The TRACEr-rail analysis indicated that most ‘train driving errors’ were ‘violations’ while most ‘train stopping errors’ were ‘errors of perception’. Both tools identified the underlying factors with the largest impact on driver error to be decreased alertness and incorrect driver expectations/assumptions about upcoming information. Overall, both tools proved useful in categorising driver errors from existing investigation reports, however, each tool appeared to neglect some important and different factors associated with error occurrence. Both tools were found to possess only moderate inter-rater reliability. It is thus recommended that the tools be modified, or a new tool be developed, for complete and consistent error classification.     

基于信息加工模型的管制员差错分类与分析

在Wickens的人类信息加工模型的基础上,加入注意功能、情景意识、内部和外部操作成形因素,建立了管制员信息处理模型。按照该模型,空中交通管制人为差错可按照认知领域分为感知与警觉性差错、短时记忆差错、长时记忆差错、判断与计划差错、响应选择差错和响应执行差错;影响管制员操作的情境条件可分为外部操作成形因素和内部操作成形因素。对以往空管人为差错的分析表明,在信息加工层面分类的人为差错更宜于确定差错的心理致因。基于该理论模型的差错分类系统可以改进人为差错分析的有效性和一致性,从而提高差错管理的有效性。     

Human actions and errors in risk handling—an empirically grounded discussion of cognitive action-regulation levels

Knowledge and error flow from the same mental sources, only success can tell the one from the other (Mach, E., 1905. Knowledge and Error. Sketches on the Psychology of Enquiry. D. Reidel Publishing Company, Dordrecht (translated into English, 1976). The current paper is concerned with human actions and errors that have accidents with an injury outcome as their consequence. Its aims are to identify and describe the occurrence of risk-triggering and risk-creating human errors, and to analyze the cognitive regulation levels of risk-triggering actions. This provides a basis on which to discuss some difficulties involved in the assigning of regulation levels to actions. The empirical material employed in the paper consists of data from 76 in-depth investigations of accidents in automated production. Risk-creating errors were found in 93% of cases, and were made at various organizational levels in the companies. The amount of and character of the risk-creating errors point to the importance of interventions that promote learning at the levels of the work team and the organization. In 88% of cases there was also a human error that triggered the risk. Risk-triggering errors were made at all cognitive-regulation levels. The conclusions concern methodological issues and theoretical question marks arising. There emerged a need to distinguish between the outcome of an action and its further consequences. Classification of regulation levels involved in human error was found to be fraught with difficulties when drawing boundaries between levels. Actions at different levels appeared to intervene and take over from each other, leaving errors at category interfaces. The structural aspect of action as a composite phenomenon might mean that it is not always possible to assign any particular act to a specific level, and since a task or an action usually is composed of several behavioral components the action could be assigned to several levels simultaneously. This raises questions concerning the applicability of the Skill–Rule–Knowledge (SRK) model to triggering errors in automated production.     

基于CREAM和贝叶斯网络的航空维修人为差错概率预测

航空维修差错不仅严重威胁着飞行安全,同时也会增加航空公司的维修成本。针对航空维修人员发生差错成因的复杂性以及历史事故数据缺乏的情况下,将人因可靠性与失误分析方法（CREAM）和贝叶斯网络（BN）相结合,提出一种改进的维修差错分析模型。根据维修任务构建相应的贝叶斯网络模型,为各子节点设置条件概率表（CPT）;基于维修基地的实际维修环境,对行为形成因子（PSFs）进行评估,得到共同绩效条件（CPCs）的水平;利用各CPC因子下各个行为功能失效模式的权重因子,对各认知活动进行失效概率的修正;将修正概率作为贝叶斯网络根节点的输入,利用推理机制,得到差错发生概率。通过案例分析和计算,验证了所述方法的可行性和有效性。     

CREAM失误概率预测法在驾驶舱机组判断与决策过程中的应用

CREAM强调人在生产活动中的绩效输出不是孤立的随机性行为,而是依赖于人完成任务时所处的环境或工作条件,它通过影响人的认知控制模式和其在不同认知活动中的效应,最终决定人的响应行为。在驾驶舱内,机组的绩效输出不仅仅是人的自身行为,还依赖于其完成任务时所处的情景环境,所以CREAM方法能够结合驾驶舱环境对机组的认知差错进行分析。在飞行中,驾驶舱内机组非常重要的一个环节是判断与决策过程,这一过程中包括询问、讨论、确定方案、执行、反馈五个环节。本文将通过分析这五个环节的相互关系及影响,以明确这种讨论过程是减少机组人为差错发生的一种有益方式,然后应用CREAM的预测法对这五个环节进行定量化分析,得出机组判断与决策过程的失误概率,完成对机组认知行为的客观评价,并为以后能够定量化研究驾驶舱内飞行员认知差错提供方法的借鉴。     

Operators’ signal-detection performance in video display unit monitoring tasks of the main control room

This study evaluated the operators’ signal-detection performance in video display unit (VDU) monitoring tasks of the main control room (MCR). So far the study of the relationship between VDU design strategy and human errors has been superficial. To validate the effect of VDUs design strategies on operating errors in the MCR, this present research adopted a within subject experimental design to develop different experimental treatments based on two types of display modes included consistent mode and mixed mode. A total of 13 undergraduate and graduate students were recruited as participants in the experiment. Dependent variables included situation awareness, reaction time, error frequency, and subjective performance. Some research support was found for the representation principles. The conclusions of this study could not only be implemented by the human-system interface (HSI) designers of a MCR in the advanced nuclear power plant (NPP) but also could be generalized to the extent that other digital workstation similar to the MCR.     

Human error taxonomies applied to driving: A generic driver error taxonomy and its implications for intelligent transport systems

Recent research indicates that driver error contributes to up to 75% of all roadway crashes. Despite this, only relatively little is currently known about the types of errors that drivers make and of the causal factors that contribute to these errors being made. This article presents an overview of the literature on human error in road transport. In particular, the work of three pioneers of human error research, Norman, Reason and Rasmussen, is scrutinised. An overview of the research on driver error follows, to consider the different types of errors that drivers make. It was found that all but one of these does not use a human error taxonomy. A generic driver error taxonomy is therefore proposed based upon the dominant psychological mechanisms thought to be involved. These mechanisms are: perception, attention, situation assessment, planning, and intention, memory and recall, and action execution. In addition, a taxonomy of road transport error causing factors, derived from the review of the driver error literature, is also presented. In conclusion to this article, a range of potential technological solutions that could be used to either prevent, or mitigate, the consequences of the driver errors identified are specified.     

基于差错分类框架辨识人为差错

为解决人因可靠性分析中的人为差错辨识问题,提出新的工作流程。在该流程中,由人为差错引导词引导,以自问自答方式开展差错辨识工作。针对动作和决策2种差错类型,分别设计差错基本分类框架。其中,动作差错基本分类框架包括5个差错大类,10个差错小类;决策差错基本分类框架包括2个差错大类,10个差错小类。这些差错小类可作为差错辨识过程中的引导词。通过2个实例,演示这2个框架在差错辨识过程中的作用。演示结果表明,在人为差错引导词引导下开展差错辨识工作,更容易保证差错辨识结果的全面性和一致性。     

民航运行中的威胁与差错管理

在民航运行中的威胁与差错管理的理论基础上,指出威胁与差错管理的目标并非消除威胁与差错,而是把其危害控制在一个可以接受的范围以内。分别界定了威胁、差错、非期望状况的概念,优化了威胁与差错管理的模型,并给予详细的实证分析。进一步丰富了威胁与差错管理的框架形式,提出了系统完善策略。该研究取得的成果有助于民航业从主动控制的角度提升安全效能。     

基于信息处理和事故链原理的结构化飞行员差错模型

为了对民航飞行员人为差错进行系统分析,研究民航业中常见人为差错的分析方法,指出其局限性。基于信息处理过程和事故链原理,从微观的认知过程和宏观的HFACS框架2个层面构建一种新的飞行员人为差错分析模型,并对模型中的情景分析、差错模式与分类、认知机理、差错恢复、差错成因、反馈机制和防御机制等几大核心模块进行阐述,重点介绍认知机理在差错形成中产生的关键影响因素,提出结构化差错分析方法的思路与步骤。该模型为民航飞行员人为差错的产生机理、差错分类和差错成因的分析提供一种新思路,但模型的进一步应用还有待于通过案例分析来验证。     

复杂工业系统中班组人因失误分析

在复杂工业系统中 ,人因可靠性分析 (HRA)是预防和减少人因失误的有效方法。人在复杂工业系统中的生产活动 ,往往是由组织中班组成员集体完成的 ,完整的HRA必须充分考虑班组人误的产生。班组人误的产生有其自身的规律 ,如何合理地定义人员行为形成因子 (PSFs)是班组人因失误分析的难点 ,也是班组人因失误分析的重要手段 ,被广泛应用在核电厂 ,航空和造船工业领域的事故分析中。笔者详细分析和探讨了班组人因失误的定义、产生过程及相关的人的行为形成因子 ,以期能使大规模工业系统中的人因失误分析更加合理和完善。     

Team mental models and team performance: a field study of the effects of team mental model similarity and accuracy

We conducted a field study of 71 action teams to examine the relationship between team mental model similarity and accuracy and the performance of real‐world teams. We used Pathfinder to operationalize team members' taskwork mental models (describing team procedures, tasks, and equipment) and teamwork mental models (describing team interaction processes) and examined team performance as evaluated by expert team assessment center raters. Both taskwork mental model and teamwork mental model similarity predicted team performance. Team mental model accuracy measures were also predictive of team performance. We discuss the implications of our findings and directions for future research. Copyright © 2006 John Wiley & Sons, Ltd.     

基于CREAM和贝叶斯网络的空管人误概率预测方法

针对研究管制人因可靠性时存在的模糊性和片面性问题，采用认知可靠性与失误分析方法（CREAM）中的扩展预测法，计算10项管制通用任务的人误概率；在此基础上，以管制行为形成因子作为根节点构建贝叶斯网络，建立其与情景控制模式的不确定关系模型，对管制员在多任务中的人误概率进行预测。研究结果表明:在由相同评判者给出行为形成因子影响效应的前提下，由CREAM扩展预测法和构建贝叶斯网络的方法预测得到的多数任务的人误概率差异较大，从方法的客观性、合理性和适用性角度分析，贝叶斯网络在研究该问题时更具优势。     

空中交通管制团队人误的分类及致因研究

复杂社会技术系统通常以团队方式运行,认识与预防团队人误是控制安全风险的重要途径。研究采用威胁与差错管理(TEM)的研究框架,以某空管运行自愿报告系统2004年全年数据为样本源,分析团队人误的流程与致因,统计发现团队人误占总体空管人误的31%。团队发现和指出错误后仍不能有效纠正错误的比例达到8%。根据样本统计并结合典型团队人误的案例,确认了空管团队人误的主要类型和分布情况以及团队行为促成因素(PSF)的框架及重要性排序,其中缺乏交流是团队人误的最常见PSF,报告样本量出现率达47.1%。最后,从组织文化建设、团队管理、标准操作程序、培训等方面提出了减少团队人误的建议。     

A real-time warning model for teamwork performance and system safety in nuclear power plants

In order to increase system safety and team performance, this study aimed to develop a real-time warning model (RTWM) by assessing team response time, error rates, and mental workload. Toward this goal, the group method of data handling (GMDH) algorithm was applied to physiological indices to predict team performance. Then fuzzy logic, fuzzy inference and linguistic variable sets representing the Team Performance and Safety Index were applied to construct the RTWM. To model the RTWM, experiments were conducted on computer-supported cooperative work (CSCW) in the personal computer transient analyzer (PCTRAN) simulator. The simulator and teamwork are designed to simulate the real tasks of the control room of the Fourth Nuclear Power Plant (FNPP) in Taiwan. In addition, important physiological parameters, the NASA-TLX questionnaire, team response time, and team error rates were collected from 39 participants. The results revealed that there was a significant positive correlation between the error rates of teamwork and the interval of event arrival time. This indicated that a pre-alarm device is necessary because vigilance decreased with time. Moreover, a predictive teamwork performance model applying the GMDH algorithm and the RTWM with a fuzzy inference system was developed in this study. The proposed model can efficiently predict teamwork performance to maintain appropriate mental workload as well as ensure system safety.     

Issues of the Human Reliability Analysis in the Context of Probabilistic Safety Studies

This article addresses methodological issues of the human reliability analysis (HRA) in the context of probabilistic safety studies. Several conventional HRA techniques, more often used for the evaluation of the human error probabilities (HEPs), have been classified. A taxonomy of human actions, failure events, and related factors is outlined in order to distinguish action phases, human behavior types and incorrect outputs (errors of omission or commission), error types (slips, lapses, and mistakes), and performance-shaping factors (PSFs) influencing the human performance. A tree is proposed to facilitate the selection of a specific method for the evaluation of human reliability with regard to attributes of the situation analyzed. A software system based on the expert system technology to facilitate and document PSA and HRA is outlined. At the end of the article some research challenges in the domain are discussed.     

复杂系统中人误原因因素的层次分析法

介绍和评析了人误分析历史上有重要影响的几种人误原因因素分类方法:传统人因分类法、信息处理分类法和认知系统工程分类法.基于认知可靠性及失误分析方法(CREAM)的人误原因因素分类,运用层次分析法(AHP)基本原理,建立了大规模复杂人-机系统人误原因因素层次结构模型及相应的AHP程序,并以JCO公司超临界事故为实例进行了分析.分析和应用结果表明,本文所建立的人误原因因素层次结构模型及AHP程序能够辨识出大规模复杂人-机系统中人误的主要原因因素,进而寻找出最优预防方案,对预防和减少此类人误事故的发生有积极意义.     

Team crystallization (SIO2): Dynamic model of team effectiveness evaluation under the dynamic and tactical environment at nuclear installation

Accident management activities at nuclear power plants require concurrent communication, information sharing, team based decision-making and collective actions under tactical and dynamic environments. Team effectiveness under such environments can be characterized as the performance of team situation awareness with hierarchically distributed information and knowledge susceptible to organizational characteristics and communication quality. This paper proposes a team performance model, called a team crystallization model comprising of four elements: state, information, organization, and orientation and its quantification method using a communication process model based on a receding horizon control approach. The team crystallization model is a holistic approach for evaluating team effectiveness in conjunction with team situation awareness considering physical system dynamics and team behavioural dynamics for a tactical and dynamic task at a nuclear power plant. This model provides a systematic measure to evaluate time-dependent team effectiveness or performance affected by multi-agents such as plant states, communication quality in terms of transferring situation-specific information and strategies for achieving the team task goal at a given time, and organizational factors. To demonstrate the applicability of the proposed model and its quantification method, a case study was carried out using the data obtained from a full-scope power plant simulator for 1000 MW(e) pressurized water reactors with four on-the-job operating groups and one expert group who knew accident sequences.