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。     

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。     

基于认知可靠性人误分析法的超市收银系统人误分析

目的拓展认知可靠性人误分析法(CREAM)研究,分析超市收银系统,找出人因失误根原因,提出改善措施。方法运用模特排时法(MOD)确定收银作业的标准时间,运用达宝易软件计算其实际时间,比较标准时间和实际时间计算人因失误率。应用CREAM的追溯分析法对此进行分析。结果找出超市收银系统中人因失误率较高的"拿物品、扫描和放钱"三个操作步骤的根原因,提出改进措施。结论 CREAM结合基础IE方法在一般工业、服务业中具有广泛的应用价值。     

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。     

CREAM在汽车零部件防错设计中的应用

运用CREAM分析到汽车零部件行业,用于指导防错设计和详细零部件设计。在介绍了CREAM方法的COCOM模型和分析步骤后,本文结合实例,详细分析和评估了CFP,从而进行优化设计。     

CREAM定量预测分析在驾驶舱人为差错事件中的应用

本文引入CREAM定量预测分析方法,对发生在驾驶舱内的人为差错事件进行定量分析,为理论研究人员对机组人为差错的研究提供一种方法的借鉴,还能对管理者提供事件分析的根本原因和差错概率,为其采取相应的措施以及制定规章制度提供合理依据。     

空中交通管制员人因可靠性定量分析研究

空中交通管制员职责是为飞行的航空器提供间隔保障,他们的不安全行为有可能导致航空器空中相撞,而对管制员行为可靠性进行有效评估是一个未解决的难题。论文首次将CREAM方法应用于空中交通管制员人因可靠性定量分析,并基于灰色系统理论,将改进的三角白化权函数用于确定CPC各因子的水平等级,以减少主观性影响。结果表明,该方法不仅能对管制员的行为可靠性进行定量预测,而且能够发现影响CPC因子中的薄弱环节,以便决策部门有针对性地制定改进措施,这对防止航空事故、保证飞行安全有着重要的意义。     

大跨度桥梁耦合颤振的全阶分析方法

基于结构的有限元全物理模型，提出了用于分析大跨度桥梁耦合颤振问题的全阶分析方法。该方法是一种单参数搜索方法，克服了以往直接颤振分析方法的一些缺陷。由于大型稀疏矩阵均为带宽压缩方式存储，并且采用高效的同时迭代方法进行求解，所以本文方法具有较高的效率。它能准确地提供系统模态频率和阻尼比随折减风速或自然风速而变化的全过程情况。此外，对主跨跨度1385m的江阴长江大桥进行了耦合颤振分析，证实了该方法在实际桥梁颤振分析中的可靠性的有效性。     

考虑人因可靠性的RCM方法改进研究

为了弥补传统以可靠性为中心的维修方法中未对人因可靠性进行分析的不足,本文提出了考虑人因可靠性的改进维修方法.该方法将人机整合系统作为分析对象,利用人因可靠性的分析方法对维修过程中的人因进行评估,以提高人机整合系统的可靠性为目标,为企业的维护决策及优化提供更系统和有效的方法论支持.该方法在石化企业选定装置的成功应用证明了其有效性,应用效果明显优于改进前.     

框架式人误原因分析技术评价研究

针对人误原因分析技术的多样性、复杂性、自身的局限性以及缺乏评价标准,文章分别从模型、分类框架和方法三个方面建立十个评价标准,即模型的简易性、全面性和真实性;分类框架的完整性、一致性和非重复性;以及方法的指导性、具体性、理解性和可靠性。依据评价标准,对选取的INCORECT、CREAM、HERA、TRACEr、HFIT和ATHEANA共6种框架式人误原因分析技术进行综合评价,为技术优劣的识别、技术的选取、结合使用以及改进提供指导。     

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.     

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.     

A fuzzy modeling application of CREAM methodology for human reliability analysis

This paper is using a fuzzy classification system for human reliability analysis in order to calculate the probability of erroneous actions according to CREAM in specific contexts e.g. maintenance tasks, in-field actions or control room operations in the running of a chemical plant. The complexities of such a system as well as the appropriate actions that have to be taken into consideration by the developers are analysed in detail. CREAM methodology has been selected among the most known and used methods for Human Reliability Analysis not only for being well-structured and precise, but also because it fits better in the general structure of the fuzzy logic. The first results from the application of the model are very promising and in accordance with CREAM. The numerical values produced by the model can be further used in the ‘classical’ risk assessment methods, such as event trees and fault trees, in order to calculate the overall occurrence frequency of a particular hazardous event. This is a pilot application that demonstrates the successful ‘translation’ of CREAM into a fuzzy logic model. However, the same model could be extended to cover also other industrial fields, like aviation technology and maritime transports.     

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 Reliability Error Analysis Method (CREAM) at the International Thermonuclear Experimental Reactor (ITER)

This paper studies how Cognitive Reliability Error Analysis Method (CREAM) can add value to the existing Quality Management System at the International Thermonuclear Experimental Reactor. We tested the CREAM framework on a sample of 71 nonconformance reports between 2016 and 2018 from the International Thermonuclear Experimental Reactor Document Management Database. All the nonconformances described in these reports relate to the incidents connected to the welding process of the vacuum vessel components. For failure categories like wrong identification, lack of attention, memory loss, or inadequate planning, CREAM provides additional information to redesign the work space. Moreover, CREAM identified quality control and management control as main underlying weaknesses that cause nonconformities. Future analysis should focus more on organizational error causes and resulting indirect links. Also, the use of a methodology like CREAM needs to be provided as special training by experts throughout all areas of activities.     

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 new model to optimize the human reliability based on CREAM and group decision making

The Cognitive Reliability and Error Analysis Method (CREAM) represents one of the second-generation approaches to human reliability assessment, taking into account the influence of environmental conditions on human error probability (HEP). In the context of CREAM, the Common Performance Conditions (CPCs) influence error probabilities. Since not all CPCs have equal impacts, this study employs the Bayesian Best Worst Method (BWM), a novel approach in group decision-making, to assign weights to these factors. Subsequently, two techniques based on basic CREAM are proposed. The current control mode is determined in the first technique according to the experts' opinions. Then the probability of human error is calculated based on the amount of control. It is possible to provide solutions for improving control mode, based on obtained results. Therefore, in this study, the second method has been used to make suggestions to enhance human reliability. For this purpose, in the second technique, an optimization problem is formulated to select the best applicable programs for managers to enhance human reliability. The proposed bi-objective model tries to increase the reliability of human resources by reducing human error and costs. The proposed bi-objective model seeks to bolster the reliability of human resources by concurrently minimizing HEP and associated costs. The efficiency of the presented methods is verified through a case study in the control room of the cement factory. The results of the first technique reveal an opportunistic control mode with a corresponding HEP of 0.0198. On the other hand, the outcomes of our proposed model underscore the greater impact of improving CPC levels in reducing the probability of human error. Ultimately, the practical programs derived from our mathematical model provide decision-makers with valuable insights to reduce the probability of human error to a mere 0.000172 through the transition from opportunistic to strategic control.     

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.