Team performance modeling for HRA in dynamic situations

This paper proposes a team behavior network model that can simulate and analyze response of an operator team to an incident in a dynamic and context-sensitive situation. The model is composed of four sub-models, which describe the context of team performance. They are task model, event model, team model and human–machine interface model. Each operator demonstrates aspects of his/her specific cognitive behavior and interacts with other operators and the environment in order to deal with an incident. Individual human factors, which determine the basis of communication and interaction between individuals, and cognitive process of an operator, such as information acquisition, state-recognition, decision-making and action execution during development of an event scenario are modeled. A case of feed and bleed operation in pressurized water reactor under an emergency situation was studied and the result was compared with an experiment to check the validity of the proposed model.     

Expert elicitation approach for performing ATHEANA quantification

An expert elicitation approach has been developed to estimate probabilities for unsafe human actions (UAs) based on error-forcing contexts (EFCs) identified through the ATHEANA (A Technique for Human Event Analysis) search process. The expert elicitation approach integrates the knowledge of informed analysts to quantify UAs and treat uncertainty (‘quantification-including-uncertainty’). The analysis focuses on (a) the probabilistic risk assessment (PRA) sequence EFCs for which the UAs are being assessed, (b) the knowledge and experience of analysts (who should include trainers, operations staff, and PRA/human reliability analysis experts), and (c) facilitated translation of information into probabilities useful for PRA purposes. Rather than simply asking the analysts their opinion about failure probabilities, the approach emphasizes asking the analysts what experience and information they have that is relevant to the probability of failure. The facilitator then leads the group in combining the different kinds of information into a consensus probability distribution. This paper describes the expert elicitation process, presents its technical basis, and discusses the controls that are exercised to use it appropriately. The paper also points out the strengths and weaknesses of the approach and how it can be improved. Specifically, it describes how generalized contextually anchored probabilities (GCAPs) can be developed to serve as reference points for estimates of the likelihood of UAs and their distributions.     

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。     

核电厂人因可靠性量化分析与应用

人因可靠性量化分析在于为概率安全评价提供量化结论并找出系统的薄弱环节.事故后人因事件的失误概率由不可恢复的认知失误概率P1、不响应概率P2与实施应急规程的关键操作动作的失误概率P3构成.采用THERP与HCR相结合的方法,分别对P1、P2和P3进行量化,在量化分析程序中给出了具体的时间分割方法与参数选取准则,并举例说明.     

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.     

核电厂HRA定性评价及应用

核电厂人的可靠性分析(HRA：Human Reliability Analysis)定性评价的目的是在定性分析的基础上,确定HRA边界条件和引入HRA模型的人因事件的数量,使得HRA的量化分析得以实施。定性评价的基本原则是确定边界假设条件,辨识出所有对安全和运行具有显著影响的人因事件序列。本文以某压水堆核电厂蒸汽传热管破裂(SGTR)为具体实例,详细分析了HRA定性评价过程,确定了分析始发事件题头的基本方法,为核电厂HRA的具体实施提供了理论指导和实践说明。     

An analysis of maintenance failures at a nuclear power plant

In this paper, a study of faults caused by maintenance activities is presented. The objective of the study was to draw conclusions on the unplanned effects of maintenance on nuclear power plant (NPP) safety and system availability. More than 4400 maintenance history reports from the years 1992–1994 of Olkiluoto BWR NPP were analysed together with the maintenance personnel. The human action induced faults were classified, e.g. according to their multiplicity and effects. This paper presents and discusses the results of a statistical analysis of the data. Instrumentation and electrical components appeared to be especially prone to human failures. Many human failures were found in safety related systems. Several failures also remained latent from outages to power operation. However, the safety significance of failures was generally small. Modifications were an important source of multiple human failures. Plant maintenance data is a good source of human reliability data and it should be used more in the future.     

Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents: Part 5: Dynamic probabilistic simulation of the IDAC model

This is the last in a series of five papers that discuss the Information Decision and Action in Crew (IDAC) context for human reliability analysis (HRA) and example application. The model is developed to probabilistically predict the responses of the control room operating crew in nuclear power plants during an accident, for use in probabilistic risk assessments (PRA). The operator response spectrum includes cognitive, emotional, and physical activities during the course of an accident. This paper describes a dynamic PRA computer simulation program, accident dynamics simulator (ADS), developed in part to implement the IDAC model. This paper also provides a detailed example of implementing a simpler version of IDAC, compared with the IDAC model discussed in the first four papers of this series, to demonstrate the practicality of integrating a detailed cognitive HRA model within a dynamic PRA framework.     

Validation and data splitting in predictive regression modeling of honing surface roughness data

Model validation is critical in predicting the performance of manufacturing processes. In predictive regression, proper selection of variables helps minimize the model mismatch error, proper selection of models helps reduce the model estimation error, and proper validation of models helps minimize the model prediction error. In this paper, the literature is briefly reviewed and a rigorous procedure is proposed for evaluating the validation and data splitting methods in predictive regression modeling. Experimental data from a honing surface roughness study will be used to illustrate the methodology. In particular, the individual versus average data splitting methods as well as the fivefold versus threefold cross-validation methods are compared. This paper shows that statistical tests and prediction errors evaluation are important in subset selection and cross-validation of predictive regression models. No statistical differences were found between the fivefold and the threefold cross-validation methods, and between use of the individual and average data splitting methods in predictive regression modeling.     

Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents: Part 1: Overview of the IDAC Model

This is the first in a series of five papers that discuss the information, decision, and action in crew context (IDAC) model for human reliability analysis (HRA). 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 during an accident for use in probabilistic risk assessments. The operator response spectrum includes cognitive, emotional, and physical activities during the course of the accident. This paper provides an overview of the IDAC architecture and principles of implementation as a HRA model. IDAC includes a crew model of three types of operators: decision maker, action taker, and consultant. Within the crew context, each individual operator's behaviors are simulated through a cognitive model under the influence of a number of explicitly modeled performance-influencing factors.     

易燃易爆岗位影响人的可靠性的主要因素

为提取影响易燃易爆工作岗位人的可靠性的因素，采用了事故资料分析与问卷调查两种方法。从３１１例事故资料的分析中，发现了影响易燃易爆工作岗位可靠性的因素主要是存在于管理与组织因素、作业者弱点、作业设计及设备缺陷３个方面。在事故资料分析的基础上，设计了问卷调查表，２６５个被试参加了调查，结果表明，作业者弱点方面的因素可以进一步细分为责任心与安全态度、文化素质与技能掌握程度、作业负荷、危险感受性及应急能力４个因素。     

Field reliability modeling based on two-dimensional warranty data with censoring times

ABSTRACT

Warranty data can be used for estimating product reliability, identifying causes of failure and designing warranty policy. Based on two-dimensional warranty data, we utilize an accelerated failure time model to investigate the effect of usage rate on product degradation. The stochastic expectation-maximization algorithm is proposed to estimate parameters of the reliability model considering both censored data and field data. Extensive simulation studies are used to validate the proposed method and to compare it with the maximum likelihood method. The utilities of the results have been demonstrated through real warranty data collected from an automobile manufacturer in China.     

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.     

Assessment of human reliability based on evaluation of plant experience: requirements and implementation

A major problem in assessment of human failures in probabilistic safety assessment is the lack of empirical data needed for human reliability analysis (HRA). This problem is aggravated by the fact that different HRA methods use different parameters for the assessment and that HRA is currently enforced to provide data and methods for assessment of human reliability in new technical environments such as computerized control rooms, in accident management situations, or in low-power and shut down situations. Plant experience is one source to deal with this problem. In this paper, a method is presented that describes how plant experience about human failures and human performance may be used to support the process of analyzing and assessing human reliability. Based on considerations of requirements of HRA, a method is presented first which is able to describe and analyze human interactions that were observed within events. Implementation of the approach as a database application is outlined. Second, the main results of the application of the method to 165 boiling water reactor events are presented. Observed influencing factors on human performance are discussed; estimates for probabilities are calculated and compared with the data tables of the THERP handbook. An outline is given for using the presented method for the analysis of cognitive errors or organizational aspects.     

Evaluating system behavior through Dynamic Master Logic Diagram (DMLD) modeling

In this paper, the Dynamic Master Logic Diagram (DMLD) is introduced for representing full-scale time-dependent behavior and uncertain behavior of complex physical systems. Conceptually, the DMLD allows one to decompose a complex system hierarchically to model and to represent: (1) partial success/failure of the system, (2) full-scale logical, physical and fuzzy connectivity relations, (3) probabilistic, resolutional or linguistic uncertainty, (4) multiple-state system dynamics, and (5) floating threshold and transition effects. To demonstrate the technique, examples of using DMLD to model, to diagnose and to control dynamic behavior of a system are presented. A DMLD-based expert system building tool, called Dynamic Reliability Expert System (DREXs), is introduced to automate the DMLD modeling process.     

A data mining approach to process optimization without an explicit quality function

In process optimization, the setting of the process variables is usually determined by estimating a function that relates the quality to the process variables and then optimizing this estimated function. However, it is difficult to build an accurate function from process data in industrial settings because the process variables are correlated, outliers are included in the data, and the form of the functional relation between the quality and process variables may be unknown. A solution derived from an inaccurate function is normally far from being optimal. To overcome this problem, we use a data mining approach. First, a partial least squares model is used to reduce the dimensionality of the process and quality variables. Then the process settings that yield the best output are identified by sequentially partitioning the reduced process variable space using a rule induction method. The proposed method finds an optimal setting from historical data without constructing an explicit quality function. The proposed method is illustrated with two examples obtained from steel making processes. We also show, through simulation, that the proposed method gives more stable results than estimating an explicit function even when the form of the function is known in advance.     

Comparison of a new expert elicitation model with the Classical Model, equal weights and single experts, using a cross-validation technique

The problem of ranking and weighting experts' performances when quantitative judgments are being elicited for decision support is considered. A new scoring model, the Expected Relative Frequency model, is presented, based on the closeness between central values provided by the expert and known values used for calibration. Using responses from experts in five different elicitation datasets, a cross-validation technique is used to compare this new approach with the Cooke Classical Model, the Equal Weights model, and individual experts. The analysis is performed using alternative reward schemes designed to capture proficiency either in quantifying uncertainty, or in estimating true central values. Results show that although there is only a limited probability that one approach is consistently better than another, the Cooke Classical Model is generally the most suitable for assessing uncertainties, whereas the new ERF model should be preferred if the goal is central value estimation accuracy.     

Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents. Part 4: IDAC causal model of operator problem-solving response

This is the fourth in a series of five papers describing the Information, Decision, and Action in Crew context (IDAC) operator response model for human reliability analysis. An example application of this modeling technique is also discussed in this series. The model has been developed to probabilistically predicts the responses of a nuclear power plant control room operating crew in accident conditions. The operator response spectrum includes cognitive, emotional, and physical activities during the course of an accident. This paper assesses the effects of the performance-influencing factors (PIFs) affecting the operators’ problem-solving responses including information pre-processing (I), diagnosis and decision making (D), and action execution (A). Literature support and justifications are provided for the assessment on the influences of PIFs.