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.     

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.     

应激情景下数字化系统操纵员认知行为失误分析

人在应激情景下的认知行为特点是第二代人因可靠性分析方法(HRA)的重点研究领域.应激因子是许多人因可靠性分析模型重点考虑的行为形成因子(PSFs)之一.本文提出基于认知资源理论的应激因子作用模型作为统一的分析框架,初步探讨了应激因子对人的认知行为失误的作用机制.运用该模型从认知资源的角度分析了数字化主控室的新特征可能导致的人因失误.     

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。     

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.     

Human-centered modeling in human reliability analysis: some trends based on case studies

As an informal working group of researchers from France, Germany and The Netherlands created in 1993, the EARTH association is investigating significant subjects in the field of human reliability analysis (HRA). Our initial review of cases from nuclear operating experience showed that decision-based unrequired actions (DUA) contribute to risk significantly on the one hand. On the other hand, our evaluation of current HRA methods showed that these methods do not cover such actions adequately. Especially, practice-oriented guidelines for their predictive identification are lacking. We assumed that a basic cause for such difficulties was that these methods actually use a limited representation of the stimulus-organism-response (SOR) paradigm. We proposed a human-centered model, which better highlights the active role of the operators and the importance of their culture, attitudes and goals. This orientation was encouraged by our review of current HRA research activities. We therefore decided to envisage progress by identifying cognitive tendencies in the context of operating and simulator experience. For this purpose, advanced approaches for retrospective event analysis were discussed. Some orientations for improvements were proposed. By analyzing cases, various cognitive tendencies were identified, together with useful information about their context. Some of them match psychological findings already published in the literature, some of them are not covered adequately by the literature that we reviewed. Finally, this exploratory study shows that contextual and case-illustrated findings about cognitive tendencies provide useful help for the predictive identification of DUA in HRA. More research should be carried out to complement our findings and elaborate more detailed and systematic guidelines for using them in HRA studies.     

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。     

Analysis of operators' performance under emergencies using a training simulator of the nuclear power plant

It is well known that there are many factors that affect the reliability of nuclear power plants (NPPs). Among them, human reliability has been considered one of the most important factors. Thus, not only in order to quantify human reliability but also to identify main causes that can degrade human reliability, various kinds of human reliability analysis (HRA) methods have been suggested and utilized in many countries. However, to perform HRA more appropriately, it is necessary to collect plant-specific or domain-specific human performance data: especially for emergencies: because they can be used to generate requisite information for HRA. From this point of view, simulator studies under emergencies may be considered important sources for obtaining human performance data.In this study, the performance data of operating crews in coping with emergencies of the reference NPP have been collected and analyzed to develop human performance database (HPDB). Since the number of collected records is 112, it can be said that extracted/analyzed results included in HPDB are statistically meaningful. Therefore, HPDB can be used not only for HRA input data but also for multiple purposes such as improving emergency operating procedures and developing advanced HRA methods.     

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.     

Human reliability analysis: a human point of view

Present human reliability analysis (HRA) is like a Centaur in that it is only half human. Error psychology is expected to provide a path leading us in the right direction. AI technology can be utilized to better understand errors. It is pointed out that the next generation HRA will require two additional components in which an analysis is made to identify two sets of error prone situations (EPSs): one in which human operators are likely to initiate critical events, and another in which they are likely to override engineered safeguard features. Carefully designed empirical studies are believed to be useful in obtaining general knowledge about critical EPSs. It is suggested that any future effort should be made based on sound psychological concepts.     

Failures without errors: quantification of context in HRA

PSA-cum-human reliability analysis (HRA) has traditionally used individual human actions, hence individual ‘human errors’, as a meaningful unit of analysis. This is inconsistent with the current understanding of accidents, which points out that the notion of ‘human error’ is ill defined and that adverse events more often are the due to the working conditions than to people. Several HRA approaches, such as ATHEANA and CREAM have recognised this conflict and proposed ways to deal with it. This paper describes an improvement of the basic screening method in CREAM, whereby a rating of the performance conditions can be used to calculate a Mean Failure Rate directly without invoking the notion of human error.     

Reliability analysis and operator modelling

The paper considers the state of operator modelling in reliability analysis. Operator models are needed in reliability analysis because operators are needed in process control systems. HRA methods must therefore be able to account both for human performance variability and for the dynamics of the interaction. A selected set of first generation HRA approaches is briefly described in terms of the operator model they use, their classification principle, and the actual method they propose. In addition, two examples of second generation methods are also considered. It is concluded that first generation HRA methods generally have very simplistic operator models, either referring to the time-reliability relationship or to elementary information processing concepts. It is argued that second generation HRA methods must recognise that cognition is embedded in a context, and be able to account for that in the way human reliability is analysed and assessed.     

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 simplified CREAM prospective quantification process and its application

Cognitive reliability and error analysis method (CREAM), with a sound cognitive model and framework with an emphasis of the whole characteristics of context, is a representative method of the so-called second generation human reliability analysis (HRA) methods and can be used in both retrospective and prospective analysis. For prospective analysis in the context of PSA/HRA, the need of CREAM to provide the results in quantitative terms is seemingly unavoidable. One remaining problem of CREAM as HRA is how to provide an easily practicable process to get the numeric results. This paper provides a simplified CREAM prospective quantification process including the basic method and extended method and their applications in the SGTR event of Qinshan 1 NPP. A comparison is made between the CREAM and THERP in the quantification and a data collection system is proposed to provide a more validated data source for future application of CREAM method.     

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.     

Human Reliability Analysis of Roof Bolting Operation in Underground Coal Mines

Investigations have shown that human error is the most common cause of roof bolting injuries. Human error probability estimation has become a critical issue for human reliability analysis (HRA) of roof bolting operation. Specialist judgment plays a crucial role in quantifying human error probability in the field because of limited availability of empirical data. However, the aggregation of specialist judgment is typically not carried out in a formal way in HRA. In this paper, an approach to combine Bayesian methodology and the success likelihood index method was to build a computable model using information from specialists for HRA of roof bolting operation. A numerical example was used to illustrate the application of the proposed methodology. Copyright © 2015 John Wiley & Sons, Ltd.     

Human reliability analysis—why not turn to the human factors community?

In response to the Dougherty thesis that contemporary human reliability analysis (HRA) methods are inadequate, this paper proposes that credible assessments of these HRA methods adequacy can be obtained only by means of their full exploitation by human factors specialists as part of the probabilistic risk assessment (PRA) process. The paper traces the history of human factors in PRA. It concludes that regarding PRA, only peripheral attention has been given to human factors; further that there has been almost a total absence of human factors specialists involved in the PRA process. The paper introduces and discusses a recent US Nuclear Regulatory Commission research initiative on a task analysis-linked evaluation technique (TALENT) concept for integrating human factors expertise into the PRA process, and fully exploiting state-of-knowledge HRA methods and data. The paper concludes that by means of TALENT implementation: (1) more credible assessments of HRA methods adequacy can be made, and (2) more realistic estimates of the overall impact of human error on complex high reliability systems' reliability and risk can be achieved.     

The application of cognitive simulation techniques to work measurement and methods analysis of production control tasks

The implementation of flexible control in production and manufacturing operations has resulted in jobs largely consisting of cognitive tasks in which performance is determined by worker goals, knowledge, and transient task-related conditions. As has been the case for traditional jobs that are characterized by predictable and/or repetitive operations, jobs that emphasize cognitive tasks also require examination. This paper presents cognitive simulation as a promising tool for work measurement and methods analysis of the cognitive tasks found within such jobs. Levels of performance, taxonomies of tasks, and the sequencing of elemental tasks are discussed from this perspective. It is noted that existing techniques for developing cognitive simulations have important similarities to the knowledge acquisition methods used to develop expert systems. Several representative techniques and their application are then discussed in terms of their potential value to work measurement and methods analysis.