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.     

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.     

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。     

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。     

Quantification of human error in maintenance using graph theory and matrix approach

Assessment of human error in maintenance requires identification of the contributing factors that lead to human error(s). These factors are called human error inducing factors (HEIFs), which take into consideration both the active and latent error contributing aspects related to man, machine and environment. A systems approach of the Graph Theory is applied in this paper for quantifying human error in maintenance activities that models the identified factors and their interactions/interrelationships in terms of human error digraph. The nodes in the digraph represent the HEIFs and the edges represent their interrelationships. The digraph is converted into an equivalent matrix and an expression based on this is developed, which is characteristic of the human error in maintenance. This expression is used to evaluate a human error index by substituting the numerical value of the factors and their interrelations. The index is a measure of the human error potential involved in the maintenance of systems. A higher value of index indicates that the error likelihood is more for the associated tasks, and more efforts are required to make the system less prone to human error. The proposed methodology is illustrated using a case study. The approach is anticipated to play a significant role in identifying sources of human errors and predicting their impact; and will help to integrate human factors during design stage with the objective of reducing human error in maintenance. Copyright © 2011 John Wiley & Sons, Ltd.     

Human Factors Effects and Analysis in Maintenance: A Power Plant Case Study

This paper proposes a methodical approach for identifying and reducing human error in maintenance activities, the human factors effect and analysis. Human factors effect and analysis presents a roadmap for selecting significant human factors affecting maintenance management as well as the most effective solutions using cost–benefit analysis. Safety and operational consequences of each human factor are compared to preventive and recovery risk controls to select the preferred risk control method. Because human factor programs are not implemented in many maintenance departments, quantitative data are rare. Thus, expert judgment may help to compare potential solutions. In order to show the applicability of the proposed approach a power plant in Kenya is selected as a case study. Procedure usage, fatigue, knowledge and experience, and time pressure are identified as the most important human factors. Training, task planning /shift management, knowledge management, scheduling as well as incident report programs are the most cost‐effective solutions for performing human factors program. The proposed approach would improve system reliability by recognizing human related failures. Furthermore, unexpected incident and accident may be reduced having knowledge about potential risk factors. Copyright © 2016 John Wiley & Sons, Ltd.     

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。     

人误模式与原因因素分析

将人误模式分为显现型和潜在型两类.显现型人误模式主要体现在时间、行为、目标、顺序四维时空特性上,而潜在的人误模式是基于认知过程与组织的,需要从微观与宏观两方面对其进行分类.影响人为失误的原因归结为个人、技术、组织、环境四类因素.将人误作为一个事件来进行分析,构建了概念上的人误原因层次诊断模型,它包括原因主因素、子因素和子子因素层等.以人误模式为切入点,对这些因素的诊断采用了人误原因的回溯性分析方法,具体过程为:一般行为条件描述-确定可能的人误模式-确定可能的人误原因因素-匹配判断.     

Estimating and ranking the impact of human error roots on power grid maintenance group based on a combination of mathematical expectation,Shannon entropy,and TOPSIS

Due to the importance of electrical grid reliability, analysis and evaluation of human error in the maintenance of electrical networks should be also considered seriously. The root causes of these errors must be identified and prioritized to plan for human error reduction. One of the objectives of the present study is to identify and predict these roots for power transmission maintenance groups from organizational, job position, communication, individual, and supervision aspects along with the relationships between these factors. In particular, this paper demonstrates that supervisor behavior as an external factor has a significant effect on maintenance personnel error. For this reason, special attention has been paid to identifying and controlling human factors from a supervisory point of view in this study. This paper also provides a method for detecting the extent of the expected influence of these roots on each personnel, since human error has a random nature. This is done based on the law of mathematical expectation. Finally, a method is suggested to rank roots based on greater effectiveness and evaluate personnel with higher error expectations. The proposed method is a combination of intermediate methods, Shannon entropy, and technique for order of preference by similarity to ideal solution (TOPSIS). The origins of the four human errors between 2014 and 2018 related to the two experts of Fars Electricity Maintenance Contractor Company are compared by the proposed method.     

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.     

Effect of maintenance policies on the just-in-time production system

Using simulation, experimental design, and regression analysis, mathematical models are developed here to describe the effect that maintenance policy, machine unreliability, processing time variability, ratio of preventive maintenance time to processing time, ratio of minimal repair time to preventive maintenance time, and production line size have on various measures of performance, namely total production line output and production line variability of the just-in-time production system. The analysis of the data shows that under different situations, different maintenance policies do not have the same effect on the production line performance. The following conclusions were obtained; when the number of production machines is low (five machines or less), and/or when the ratio of minimal repair time to preventive maintenance time is high, maintenance policy III leads to a higher performance than maintenance policy II. Otherwise maintenance policy II, which is more sensitive to the change of the ratio of minimal repair time to preventive maintenance time, leads to a higher performance. The results of the study can be utilized in choosing a maintenance policy as a function of the production process parameters. Once a policy is chosen, the practitioner can select the most important factors to control under that policy in order to minimize the machine idle time, maximize the production process reliability, improve productivity, and therefore increase the production line performance.     

Human reliability analysis for manned submersible diving process based on CREAM and Bayesian network

The diving mission of manned submersibles is a long‐term, high‐intensity work that is affected by many factors and is in a narrow confined space. In order to improve the reliability of oceanauts' safe operations, this paper is based on the cognitive reliability and error analysis method (CREAM) and the Bayesian network method to study the human errors of the diving mission. First, we construct a Bayesian network framework of the diving process by analyzing the diving steps. Second, the CREAM is applied to calculate the prior probability of each root node's error. Then, the backward reasoning ability of the Bayesian network is used to calculate the posterior probabilities and identify the top few risk nodes. Finally, we obtained the top few risk factors. Among them, we find that the light distribution design in the risk nodes is the more influential risk factor, so a brief design is made on them.     

人因可靠性分析方法CREAM及其应用研究

文章在系统分析了第一代人因可靠性分析方法存在的不足的基础上，详细地介绍了第二代人因可靠性分析方法CREAM。包括CREAM独特的认知模型、前因/后果分类方案和分析技术。并结合一个实例详细描述了CREAM的具体应用过程。     

Modeling and simulating the impact of forgetting and communication errors on delays in civil infrastructure shutdowns

Handoff processes during civil infrastructure operations are transitions between sequential tasks. Typical handoffs constantly involve cognitive and communication activities among operations personnel, as well as traveling activities. Large civil infrastructures, such as nuclear power plants (NPPs), provide critical services to modern cities but require regular or unexpected shutdowns (i.e., outage) for maintenance. Handoffs during such an outage contain interwoven workflows and communication activities that pose challenges to the cognitive and communication skills of handoff participants and constantly result in delays. Traveling time and changing field conditions bring additional challenges to effective coordination among multiple groups of people. Historical NPP records studied in this research indicate that even meticulous planning that takes six months before each outage could hardly guarantee sufficient back-up plans for handling various unexpected events. Consequently, delays frequently occur in NPP outages and bring significant socioeconomic losses. A synthesis of previous studies on the delay analysis of accelerated maintenance schedules revealed the importance and challenges of handoff modeling. However, existing schedule representation methods could hardly represent the interwoven communication, cognitive, traveling, and working processes of multiple participants collaborating on completing scheduled tasks. Moreover, the lack of formal models that capture how cognitive, waiting, traveling, and communication issues affect outage workflows force managers to rely on personal experiences in diagnosing delays and coordinating multiple teams involved in outages. This study aims to establish formal models through agent-based simulation to support the analytical assessment of outage schedules with full consideration of cognitive and communication factors involved in handoffs within the NPP outage workflows. Simulation results indicate that the proposed handoff modeling can help predict the impact of cognitive and communication issues on delays propagating throughout outage schedules. Moreover, various activities are fully considered, including traveling between workspaces and waiting. Such delay prediction capability paves the path toward predictive and resilience outage control of NPPs.     

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.     

Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents. Part 2: IDAC performance influencing factors model

This is the second in a series of five papers describing the information, decision, and action in crew context (IDAC) model for human reliability analysis. An example application of this modeling technique is also discussed in this series. The model is developed to probabilistically predict the responses of the nuclear power plant control room operating crew in accident conditions. The operator response spectrum includes cognitive, psychological, and physical activities during the course of an accident. This paper identifies the IDAC set of performance influencing factors (PIFs), providing their definitions and causal organization in the form of a modular influence diagram. Fifty PIFs are identified to support the IDAC model to be implemented in a computer simulation environment. They are classified into eleven hierarchically structured groups. The PIFs within each group are independent to each other; however, dependencies may exist between PIFs within different groups. The supporting evidence for the selection and organization of the influence paths based on psychological literature, observations, and various human reliability analysis methodologies is also indicated.     

Training needs analysis—a human factors analysis tool

Human factors (HF) engineers have contributed to the reduction of human error, by improving user performance through the design of the user interface. However, there still remains a requirement for user training. This is especially true for complex, high risk processes employing new technology. Reduced manning and downskilling are putting increased pressures on users who, in many activities, still remain the last level of safety protection. In responding to this need, HF engineers now employ a range of tools and methods during the design process, which assist in identifying the training media and training design. Training needs analysis (TNA) offers a structured methodology to facilitate the information gathering process and identify any shortfall between current user performance and that demanded by new technology and designs. By the use of these methods and tools the potential for human error can be reduced, for the following reasons: (a) improved user performance (b) better designed and more efficient training systems. Existing TNA methods have been better suited to the study of existing equipment. This paper describes the training requirements process, and proposes a usable methodology for contractors conducting a TNA in the design of new systems/equipment. It is of particular use where information on the operation or maintenance may be limited.     

Is human failure a stochastic process?

Human performance results in failure events that occur with a risk-significant frequency. System analysts have taken for granted the random (stochastic) nature of these events in engineering assessments such as risk assessment. However, cognitive scientists and error technologists, at least those who have interest in human reliability, have, over the recent years, claimed that human error does not need this stochastic framework. Yet they still use the language appropriate to stochastic processes. This paper examines the potential for the stochastic nature of human failure production as the basis for human reliability analysis. It distinguishes and leaves to others, however, the epistemic uncertainties over the possible probability models for the real variability of human performance.     

人因可靠性分析的新方法——ATHEANA:原理与应用

探讨了传统的人因可靠性分析方法的特点及局限性，介绍了一种新型的人因可靠性分析方法－－ATHEANA法的基本思想和概念、所基于的模型、分析框架、实施步骤及特点。最后，给出了ATHEANA法在核电站的一个应用实例。     

Understanding maintenance work in safety-critical organisations – managing the performance variability

Human and organisational performance variability has been identified as a cause of many latent and active failures in maintenance. Seldom has the variability been considered as an integral and inherent part of the maintenance activity to be managed by organisational means. The article deals with the challenge of understanding maintenance work in safety-critical organisations. The aim is to review the current literature on maintenance work and illustrate the organisational research challenges of managing performance variability in maintenance. This article presents six major research challenges in managing performance variability in maintenance. The article concludes by noting that a holistic theory on maintenance work is needed to manage the variability and turn it into a positive force. Maintenance has the potential to produce positive performance variability and guard against negative instability in complex sociotechnical systems.