基于行为形成因子相关性的人因失误影响研究

生产过程中,行为形成因子会对人因失误产生不可忽略的作用。在行为形成因子诱发人因失误的过程中,其他行为形成因子也会对诱发过程产生影响。本文基于这种影响作用,对行为形成因子影响人因失误的程度进行了量化,求解得出了人因失误的概率,并进行了实验验证。     

工业系统监控作业中的人因失误研究综述

目的 对工业系统监控作业中的人因失误相关研究进行梳理和分析,以了解其研究现状及发展趋势。方法 通过文献研究法,梳理并界定人因失误的概念,归纳出人因失误的性质与特征。通过对监控作业的演变研究,总结出目前工业系统数字化监控作业的人因特征。从核电、航空、轨道交通、电力系统等行业出发,分析监控作业人员的认知行为模型,探究人因失误产生机制,梳理人因失误辨识方法,分析其在工业系统监控作业中的适用性,识别监控作业中人因失误的影响因素。结论 随着数字化监控界面的发展及作业情境的变化,监控作业人员的认知过程和行为响应方式都发生了改变,需要不断丰富人因失误理论,探究人因失误机理,为工业系统监控作业中人因失误管控提供理论依据。     

煤矿井下作业人因可靠性

为了研究煤矿井下作业人员连续性作业的人因可靠性,提出一种人因可靠性分析（HRA）方法--认知可靠性和失误分析方法（CREAM）。通过对CREAM方法中共同绩效因子（CPC）、环境影响指数β和控制模式区域进行了修正,使其符合煤矿井下作业情景环境。通过实证得出煤矿井下掘进钻眼工作业失效概率为0025,通过降低测量钻眼角度及深度最可能失效模式概率,可有效提高煤矿企业井下作业人因可靠性。得出结论：CREAM修正方法为基础建立的控制模式与共同绩效因子模型,减少了人为参与的不确定性,具有较好的可操作性。     

核电厂操纵员的认知行为和失误分析

为了识别核电厂数字化主控室操纵员的认知行为特性和主要认知失误,基于操纵员的认知任务分析,从监视、状态评估、响应计划和执行四个"认识域"来构建操纵员认知行为模型,并发展基于模型的认知失误分类,进一步识别出操纵员的认知行为分布。结果表明,操纵员的认知行为主要表现为简单的技能型和规则型认知行为,减少了复杂的知识型认知行为。在此基础上,通过核电厂小偏差和事件报告分析,识别出操纵员的认知失误分布及其原因,主要失误包括看/听、认读、状态解释失误、操作目标错误以及错误的操作等,主要原因涉及人-机界面设计、培训和监管等问题。     

综掘工作面人因失误模型

煤矿综掘工作面是由人、机器设备和环境组成的复杂生产系统,其工作面环境条件易变、作业空间狭窄、视觉环境差等,对机器设备的可靠性,以及工人的作业效率及身心健康都有重要的影响。通过理论和实际调研的结合,在研究综掘工作面人-机-环境关系的基础上,借鉴统计理论,建立人因失误模型,运用参数估计进行统计分析,量化人因失误致因因子,求解人子系统的可靠性指标,深入分析人因失误行为发生的类型、行为因子。     

数字化核电厂班组情景意识失误研究

为更好地研究数字化核电厂TSA失误,通过核电站现场调研和模拟仿真实验观察,从宏观认知过程的角度构建TSA认知模型,发展一个基于模型的TSA失误分类,结合系统论观点构建TSA失误的行为影响因素(PIFs)分类以及从人的信息处理过程角度建立TSA失误的心理失误机制(PEM)分类。采用建立的分类体系对收集到的大量人因事件报告进行统计分析,获得145组样本数据,识别出主要的TSA失误模式,PIFs和PEM,为数字化核电厂控制和减少TSA失误提供理论支持。     

检验THERP合理性及不足的实验研究——以汽车冷媒加注工序为例

为了检验人的失误概率预测技术（technique of human error rate prediction，THERP）的合理性及不足，以汽车内饰冷媒加注工序为例，采用E-prime实验的方法分析了冷媒加注工序的人因失误概率。通过比较发现，汽车冷媒加注工序的4个任务的人因失误概率的实验结果与THERP的理论计算结果基本一致，说明了THERP方法的合理性。并说明了THERP的不足在于计算人因失误时忽略了操作行为中的认知过程。     

基于改进成功拟然指数法的管制员调配飞行冲突的人因失误概率研究

目的为了研究管制员在调配飞行冲突时的人因失误的可能性,探究造成其人因失误的行为形成因子(Performance Shaping Factors,PSF),确定其在PSF影响下的人因失误概率。方法首先结合实际情况总结归纳了八种管制工作中可能出现的人因失误,并通过调查和计算得到每个人因失误的相对可靠度;应用认知可靠性与差错分析方法(Cognitive Reliability and Error Analysis Method,CREAM)提取并改进行为因子(PSF),并通过模糊层次分析法计算各个行为因子的归一化权重,然后计算得到SLIM中的成功似然指数(Success Likelihood Index,SLI);最后通过Delphi法确定端点概率值,进而求得管制员人因失误的概率。结果本文总结归纳得到了八种管制员人为失误,并采用德尔菲法(Delphi)与成功似然指数法(Success Likelihood Index Method,SLIM)相结合的方法分析管制员调配飞行冲突时的人因失误概率。结论本文应用改进的SILM方法定量与定性的计算了管制员调配飞行冲突时的人因失误概率,符合实际情况,在目前管制员人因失误研究缺乏数据和模型的情况下,具有一定指导意义。     

基于ACT-R模型的汽车冷媒加注工序人因防错研究

为了对汽车装配中冷媒加注工序进行有效的人因防错设计,应用ACT-R认知模型分析操作行为的人因失误。对汽车冷媒加注操作进行ACT-R认知行为建模,并运用CogTool软件仿真,得出空调类型与车型不匹配的错误和管路辨识错误是冷媒加注工序中的主要人因失误,验证了ACT-R认知模型应用到冷媒加注操作人因失误分析的有效性。并结合防错设计的消除、检测、替代、简化、减少等思路,对冷媒加注生产工序进行了人因防错设计。企业的反馈评价说明了本防错设计的可行性和有效性。     

组织人因失误分析

工业生产中，人因失误已成为最主要的事故源之一。人因失误研究越来越受到重视，研究方向已从个体失误向组织人误发展。文章论述了组织人因失误产生的过程，辨识出组织人因失误行为形成因子(PSFs)并归纳成为组织人因失误的4大类12组原因因素，提出了预防组织人因失误的基本对策。     

A Review of Cognitive Models in Human Reliability Analysis

Human error behavior is determined by both environmental and human factors. In particular, psychological and spiritual factors have a decisive impact on human errors. The human cognitive model not only makes a sound exposition of the generation process and mechanism of human erroneous actions but also improves the accuracy and credibility of human reliability analysis (HRA). Therefore, it helps effectively avoid and prevent human errors in industrial fields. This paper highlights the significant role that the cognitive model has played in HRA. Then, based on an analysis of the nature of human behavior and the classifications of common human errors, several typical cognitive models are summarized in the areas of ergonomics, behavioral science, and cognitive engineering, including a cognitive model related to process, an information‐processing model, a decision‐making and problem‐solving process model, and a cognitive simulation model based on computer technology. Then, cognitive models and the corresponding HRA methods that are applied in the fields of reliability engineering, safety engineering, and risk assessment are reviewed. Finally, some directions and challenges are proposed for the future research of cognitive models applied in HRA methods based on the discussion of current cognitive models used in HRA methods. Copyright © 2016 John Wiley & Sons, Ltd.     

人的可靠性综合分析模式及应用

提高系统可靠性的关键步骤是提高系统中人的可靠性,这需要对人的可靠性进行分析。当前分析人的可靠性主要依靠运用各类HRA模型,这些模型各有优缺点。为了研究航空人为差错,选取了具有代表性的3个HRA模型,对人的可靠性分析模型THERP（technique for human error rate prediction)、CREAM（cognitive reliability and error analysis method)、IDAC(information decision and action)进行了分析。将3种模型进行比较,找出它们的优劣之处,结合3种模型的优点,建立了以THERP模型、CREAM模型以及IDAC模型为主体的人的可靠性综合分析模式,并将该分析模式在航空人为差错分析上进行了应用,并给出实例说明该分析模式的应用。     

An extended CREAM model based on analytic network process under the type-2 fuzzy environment for human reliability analysis in the high-speed train operation

Human error is one of the largest contributing factors to unsafe operation and accidents in high-speed train operation. As a well-known second-generation human reliability analysis (HRA) technique, the cognitive reliability and error analysis method (CREAM) has been introduced to address HRA problems in various fields. Nevertheless, current CREAM models are insufficient to deal with the HRA problem that need to consider the interdependencies between the Common Performance Conditions (CPCs) and determine the weights of these CPCs, simultaneously. Hence, the purpose of this paper is to develop a hybrid HRA model by integrating CREAM, the interval type-2 fuzzy sets, and analytic network process (ANP) to overcome this drawback. Firstly, the interval type-2 fuzzy sets are utilized to express the highly uncertain information of CPCs. Secondly, the ANP is incorporated into the CREAM to depict the interdependencies between the CPCs and determine their weights. Furthermore, human error probability (HEP) can be calculated based on the obtained weights. Finally, an illustrative example of the HRA problem in high-speed train operation is proposed to demonstrate the application and validity of the proposed HRA model. The results indicate that experts prefer to express their preferences by fuzzy sets rather than crisp values, and the interdependences between the CPCs can be better depicted in the proposed model.     

Structured information analysis for human reliability analysis of emergency tasks in nuclear power plants

Being supported by scarce empirical data, most of the performance influencing factors in human reliability analysis (HRA) have to be assessed on the basis of the analyst's knowledge on the human performance in given tasks and their context. Therefore, the outcome of HRA may only be warranted by a proper application of their knowledge based on sufficient information about the tasks and situations. However, most of the HRA methodologies, including the newly developed ones, focus on the provision of cognitive models, error mechanisms, error types and analysis method while leaving the information collection mostly in the hands of the analyst. This paper suggests structured information analysis (SIA), which helps HRA analysts in collecting and structuring such information on tasks and contexts. The SIA consists of three parts: the scenario analysis, the goal-means analysis, and the cognitive function analysis. An expert evaluation showed that this three-part information analysis allowed more expressiveness and hence more confidence on the error prediction than ASEP HRA.     

Human error data collection as a precursor to the development of a human reliability assessment capability in air traffic management

Quantified risk and safety assessments are now required for safety cases for European air traffic management (ATM) services. Since ATM is highly human-dependent for its safety, this suggests a need for formal human reliability assessment (HRA), as carried out in other industries such as nuclear power. Since the fundamental aspect of HRA is human error data, in the form of human error probabilities (HEPs), it was decided to take a first step towards development of an ATM HRA approach by deriving some HEPs in an ATM context.This paper reports a study, which collected HEPs via analysing the results of a real-time simulation involving air traffic controllers (ATCOs) and pilots, with a focus on communication errors. This study did indeed derive HEPs that were found to be concordant with other known communication human error data. This is a first step, and shows promise for HRA in ATM, since HEPs have been derived which could be used in safety assessments, although these HEPs are for only one (albeit critical) aspect of ATCOs’ tasks (communications). The paper discusses options and potential ways forward for the development of a full HRA capability in ATM.     

Identification and Assessment of Factors Influencing Human Reliability in Maintenance Using Fuzzy Cognitive Maps

Human element forms an inevitable part of maintenance activity and gets affected by a variety of interacting factors, ranging from environmental, organizational, job factors, and so on to personal characteristics, which bring in inherent variability in its reliability. Assessment of impact of these factors is, therefore, critical for human reliability estimation in maintenance. In every probabilistic risk, safety or maintenance analysis, human reliability does act as an effective aspect to assess implications of various aspects of the human performance. But the main constraint with various human reliability analysis methods is in judging the important human performance influencing factors. Because of high degree of uncertainty and variability that characterizes the plant maintenance environment, it is proposed to use the soft computing technique of fuzzy cognitive maps in exploring the importance of performance shaping factors in maintenance scenario. For this purpose, the maintenance environment is modeled in terms of factors affecting human reliability using cognitive maps. The causal relationships among these factors are explored and simulations performed to quantify its effect on the human reliability. The applicability of the methodology is demonstrated through an example. Copyright © 2013 John Wiley & Sons, Ltd.     

Review of advances in human reliability analysis of errors of commission—Part 2: EOC quantification

In close connection with examples relevant to contemporary probabilistic safety assessment (PSA), a review of advances in human reliability analysis (HRA) of post-initiator errors of commission (EOCs), i.e. inappropriate actions under abnormal operating conditions, has been carried out. The review comprises both EOC identification (part 1) and quantification (part 2); part 2 is presented in this article. Emerging HRA methods in this field are: ATHEANA, MERMOS, the EOC HRA method developed by Gesellschaft für Anlagen- und Reaktorsicherheit (GRS), the MDTA method and CREAM. The essential advanced features are on the conceptual side, especially to envisage the modeling of multiple contexts for an EOC to be quantified (ATHEANA, MERMOS and MDTA), in order to explicitly address adverse conditions. There is promising progress in providing systematic guidance to better account for cognitive demands and tendencies (GRS, CREAM), and EOC recovery (MDTA). Problematic issues are associated with the implementation of multiple context modeling and the assessment of context-specific error probabilities. Approaches for task or error opportunity scaling (CREAM, GRS) and the concept of reference cases (ATHEANA outlook) provide promising orientations for achieving progress towards data-based quantification. Further development work is needed and should be carried out in close connection with large-scale applications of existing approaches.     

A method of human reliability analysis and quantification for space missions based on a Bayesian network and the cognitive reliability and error analysis method

Analyses of human reliability during manned spaceflight are crucial because human error can easily arise in the extreme environment of space and may pose a great potential risk to the mission. Although various approaches exist for human reliability analysis (HRA), all these approaches are based on human behavior on the ground. Thus, to appropriately analyze human reliability during spaceflight, this paper proposes a space‐based HRA method of quantifying the human error probability (HEP) for space missions. Instead of ground‐based performance shaping factors (PSFs), this study addresses PSFs specific to the space environment, and a corresponding evaluation system is integrated into the proposed approach to fully consider space mission characteristics. A Bayesian network is constructed based on the cognitive reliability and error analysis method (CREAM) to model these space‐based PSFs and their dependencies. By incorporating the Bayesian network, the proposed approach transforms the HEP estimation procedure into a probabilistic calculation, thereby overcoming the shortcomings of traditional HRA methods in addressing the uncertainty of the complex space environment. More importantly, by acquiring more information, the HEP estimates can be dynamically updated by means of this probabilistic calculation. By studying 2 examples and evaluating the HEPs for an International Space Station ingress procedure, the feasibility and superiority of the developed approach are validated both mathematically and in a practical scenario.     

Representing cognitive activities and errors in HRA trees

A graphic representation method is presented herein for adapting an existing technology—human reliability analysis (HRA) event trees, used to support event sequence logic structures and calculations—to include a representation of the underlying cognitive activity and corresponding errors associated with human performance. The analyst is presented with three potential means of representing human activity: the NUREG/CR-1278 HRA event-tree approach; the skill-, rule- and knowledge-based paradigm; and the slips, lapses, and mistakes paradigm. The above approaches for representing human activity are integrated in order to produce an enriched HRA event tree—the cognitive event tree system (COGENT)—which, in turn, can be used to increase the analyst's understanding of the basic behavioral mechanisms underlying human error and the representation of that error in probabilistic risk assessment. Issues pertaining to the implementation of COGENT are also discussed.