Human Reliability Analysis Model Research Articles

Identifying human failure events (HFEs) for external hazard probabilistic risk assessment

In recent years, several advancements in nuclear power plant (NPP) probabilistic risk assessment (PRA) have been driven by increased understanding of external hazards, plant response, and uncertainties. However, major sources of uncertainty associated with external hazard PRA remain. One important source is how risk-significant human actions that are carried out to enable plant response and recovery from natural hazards cause the close coupling of physical impacts on plants and overall plant risk during these hazard events. This makes human reliability and human-plant interactions important elements to consider in resolving PRA gaps in external hazards. One of the challenges in considering human response in external hazard probabilistic risk assessment (XHPRA) is that most existing human reliability analysis (HRA) models were not developed for assessing actions outside the control room (termed ex-control room actions) and hazard response. To support this new scope, HRA models will need to be developed or modified to support identification of human activities, causal factors, and uncertainties inherent in external hazard response, thereby providing insights regarding event timing and physical event conditions as they relate to human performance. In this study, there are two main objectives: (1) evaluate the applicability of an existing cognitive-based HRA method, Phoenix, to ex-control room actions, and (2) identify sources of uncertainty to be characterized or reduced in order to make this method suitable for XHPRA. The first step of such work is performed by assessing the suitability of existing HRA methods to support identifying human failure events (HFEs) for human response to flooding hazards. These HFEs are human actions or inactions that are involved in human responses to flooding hazards and could contribute to the loss of a critical function for the plant in the scenario being examined. In this work, decomposition analyses using the cognitive-based Phoenix HRA model are used to identify HFEs. The Phoenix method was found to be suitable for analyzing ex-control room actions as well as identifying specific HFEs and underlying crew failure modes (CFMs). However, the method's suitability for use in ex-control room actions would benefit from expanding the available CFMs to accommodate a larger variety of physical and communication tasks.

An HRA Model Based on the Cognitive Stages for a Human-Computer Interface in a Spacecraft Cabin

HRA (Human Reliability Analysis) can be seen as a symmetric problem, as it is mainly reflected in two aspects of failure and success. Human error is the most common accident in industrial systems; furthermore, an astronaut is in a very complex environment, and coupled with weightlessness, it is easy to cause human error. For this reason, this paper took the human-computer interface in a spacecraft cabin as the background, and based on the literature, questionnaire inquiry, and the division of three cognitive processes during the interaction between the astronaut and human, a computer interface was determined. This paper proposed a human reliability influencing factors system of different cognitive phases for the human-computer interface in a spacecraft cabin, a task analysis tree with a symmetry of success and failure, an HRA model with symmetry of failure and success based on cognitive stages, and Game Theory and Fuzzy Center of Gravity Method, and obtained influencing factors weights of three cognitive stages. By simulating an experiment, the trend of error probability curves shows the rationality of the human reliability method. Finally, an example was illustrated, and the analysis process of the example demonstrates that an HRA model with symmetry provides a feasible analysis process and method for the cognitive reliability of the spacecraft cabin human-computer interface interaction. The research achievements in this paper can provide theoretical guidance to improve human error root reason analysis, an analysis basis of how to improve influencing factors level, and provide a HRA method based on cognitive stages for the human-computer interaction process in a spacecraft cabin.

Cognitive Behavioral Model of an Operation Crew in the Main Control Room of a Nuclear Power Plant Based on a State-Oriented Procedure

The team’s cognitive behavior plays a crucial role in dealing with accidents at nuclear power plants. Herein, the main behaviors of reactor operators and coordinators in performing accident management were analyzed in executing a state-oriented procedure. According to these cognitive behavioral characteristics, we established cognitive behavioral models of accident management procedures. After that, a cognitive behavioral model was established for the team in the main control room of the nuclear power plant based on the two models, which is expected to provide support to the optimization of a corresponding Human Reliability Analysis model.

Effects of Automation for Emergency Operating Procedures on Human Performance in a Nuclear Power Plant.

The digitization of the control systems in the main control room of a nuclear power plant has changed the operators’ role in coping with accidents and has thus brought about new human factor problems. This article focuses on the procedures that are used for guiding the emergency operating procedures in a nuclear power plant, and experimentally investigates the effects of the digitization of procedures on operators’ mental workload and situation awareness. In these experiments, the procedures at three different levels of automation, namely, paper-based procedures (PBPs), electronic procedures (EPs), and computer-based procedures (CBPs), are used as the independent variables. According to the experimental results, using these procedures at a high level of automation enables the operator to exhibit favorable operational performance with a decreased mental workload; however, the operator’s situation awareness (SA) is decreased. The research results presented here can provide a reference level for optimally setting the level of automation of the emergency operating procedures in a nuclear power plant and provide support for the optimization of a corresponding HRA (Human Reliability Analysis) model.

Crew performance variability in human error probability quantification: A methodology based on behavioral patterns from simulator data

Current Human Reliability Analysis models express error probabilities as a function of task types and operational context, without explicitly modelling the influence of different crew behavioral characteristics on the error probability. The influence of such variability is treated only implicitly, by variability and uncertainty distributions with bounds primarily obtained by expert judgment. This paper presents a methodology to empirically incorporate crew performance variability in error probability quantification, from simulator data. Crew behaviors are represented by a set of “behavioral patterns” that emerge in the observation of operating crews (e.g. in information sharing or in adhering to procedural guidance). The paper demonstrates the use of a Bayesian hierarchical model to explicitly capture the performance variability emerging from data. The methodology is applied to a case study from literature. Numerical demonstrations are performed in order to compare the proposed approach to the existing quantification models used in HRA for treating simulator data.

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

AbstractHuman 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.

Considerations for the Development of a Human Reliability Analysis (HRA) Model Oriented to the Maintenance Work Safety

Within the field of the Industrial Technology, maintenance of facilities and equipment is considered a key issue for the productive activity, although throughout history, engineering research efforts have been oriented mostly toward design than the maintenance of the facilities. From this point of view, an important effort has been made, especially in the last two decades, to improve safety aspects related to Industrial Maintenance, from two standpoints: on one hand, maintenance guarantees a safer operation activity, and in the other hand, the accident rate in maintenance activity had to be minimized.It is commonly accepted in specialized literature that Human Error may be at the origin of around 70% of industrial accidents, and this generates a special interest in the Human Error Analysis. Certainly Human Reliability Analysis (HRA) Techniques are not anything new, we can say that since early eighties of the 20th century, a lot of methods and models have been developed, many of them with a general character, but very focused on specific fields, such as nuclear or aeronautical industry, it is mainly due to the fact that accidents in these sectors could potentially have severe consequences.The main purpose of this paper is to establish basis for a farther development of a Human Reliability Analysis Model for industrial maintenance. This works has a cross-sectoral approach, including different fields and activities, whose object is to minimize accident risks and incidents in their earliest phase. For this, an exhaustive review of the available models of Human Reliability Analysis Models in the specialized literature from the two generations developed to date has been carried out, those models has been classified according to their characteristics and their usefulness, in order to detect which aspects are appropriate to the maintenance activity. The aim is to proceed towards a generalized Human Reliability Analysis model that can be used for the evaluation of maintenance work through a wide range of activities.

A fuzzy and Bayesian network CREAM model for human reliability analysis – The case of tanker shipping

This paper proposes a quantitative human reliability analysis (HRA) model based on fuzzy logic theory, Bayesian network, and cognitive reliability & error analysis method (CREAM) for the tanker shipping industry. The common performance conditions (CPCs) in conventional CREAM approach are custom-modified to better capture the salient aspects of the situations and conditions for on-board tanker work. Fuzzy logic technique using triangle and trapezoidal membership functions is applied to model the uncertainty and ambiguity of the CPCs as well the control modes in CREAM. A Bayesian network reasoning model using the membership of CPCs as inputs is developed which determines the probability distribution of the control modes. Human error probability (HEP) is obtained from memberships of the control modes and the results of Bayesian network reasoning. A case study in tanker shipping industry with 18 crew members is provided, and the results show that the evaluation of HEP according to the proposed HRA model is very promising and the HRA model is consistent with the original CREAM approach. The sensitivity of the model is also checked against the inputs of the crew members. It is concluded that the enhanced HRA model is able to provide reliable human performance failure results.

Developing the foundations of a cognition-based human reliability analysis model via mapping task types and performance-influencing factors: Application to radiotherapy

Most human reliability analysis methods have been developed for nuclear power plant applications; this challenges the application of the available techniques to other domains. Indeed, for application to a specific domain, a human reliability analysis method should address the relevant tasks and performance conditions. The aim of this article is to propose a methodology to develop a generic task type–performance-influencing factor structure, specific for application to a domain of interest and directly linked to an underlying cognitive framework of literature. The structure provides the foundation of a human reliability analysis method built on the generic task type concept; it identifies the sector-specific performance-influencing factor effects on the failure probability that the method needs to represent and quantify for each generic task type. The methodology is intended to support a systematic and traceable process to develop the generic task type–performance-influencing factor structure, to ease the review of the process and of its results and, in case, identify and implement changes to the structure. The proposed methodology is applied to the radiotherapy domain allowing the development of sector-specific taxonomies of representative critical tasks, their failure modes, underlying cognitive failure mechanism, and influencing performance-influencing factors. This is part of a broader activity carried out by the Risk and Human Reliability Group at the Paul Scherrer Institute of Switzerland to develop a human reliability analysis method, specific for the radiotherapy domain. The activity is conducted in close cooperation with Paul Scherrer Institute’s Center for Proton Therapy, where a first application of the method is foreseen.

Incorporating individual differences in human reliability analysis: An extension to the virtual experimental technique

The data required to perform Human Reliability Analysis (HRA) for emergency conditions are not readily available and are difficult to retrieve from accident investigations. In the absence of emergency conditions data, the conventional approach of gathering data for HRA is using expert judgment. Expert judgment often suffers from uncertainty, subjectivity, and incompleteness, which makes the reliability of this data collection technique questionable. A more recent approach is to collect data by conducting experiment in virtual environments with human subjects. Though virtual experimental technique addresses the issues of uncertainty, subjectivity, and incompleteness, it still does not consider individual differences while assigning the influence of different factors on human performance. This paper proposes to advance the virtual experimental technique by enabling the consideration of individual differences. An experiment using virtual environment was done to observe performances of 36 individuals during offshore emergency evacuation. By integrating the data collected from the virtual environment into an HRA model, the reliability of each individual was assessed. Sensitivity analysis was then performed to identify the most influential factors that contributed to failure in emergency conditions. This analysis can help identify specific weaknesses that a participant might have. For example, if a participant is found to be more sensitive to a particular factor, then training scenarios with different variations of the factor can be provided to the participant until an accepted level of competency is reached. Identification of a weakness can be combined with adaptive human factor training so that each individual can obtain competence more quickly.

Methods for building Conditional Probability Tables of Bayesian Belief Networks from limited judgment: An evaluation for Human Reliability Application

The present paper evaluates five methods for building Conditional Probability Tables (CPTs) of Bayesian Belief Networks (BBNs) from partial expert information: functional interpolation, the Elicitation BBN, the Cain calculator, Fenton et al. and Røed et al. methods. The evaluation considers application to a specific field of risk analysis, Human Reliability Analysis (HRA). The five methods are particularly suited for HRA models calculating the human error probability as a function of influencing factor assessments. The performance of the methods is evaluated on two simple examples, designed to test aspects relevant for HRA (but not exclusively): the representation of strong factor influences and interactions, the representation of uncertainty on the BBN relationships, and the method requirements as the BBN size increases. The evaluation underscores modelling limitations related to the treatment of multi-factor interdependencies and of different degrees of uncertainty in the factor relationships. The functional interpolation method is the least susceptible to these limitations; however, its elicitation requirements grow exponentially with the model size. Besides expert judgment, HRA applications of BBNs include the use of empirical data, combination of data and judgment, information from existing HRA methods: the building of the CPTs in these applications is outside the scope of the evaluation.

Human reliability under sleep deprivation: Derivation of performance shaping factor multipliers from empirical data

This paper develops a probabilistic approach that could use empirical data to derive values of performance shaping factor (PSF) multipliers for use in quantitative human reliability analysis (HRA). The proposed approach is illustrated with data on sleep deprivation effects on performance. A review of existing HRA methods reveals that sleep deprivation is not explicitly included at present, and expert opinion is frequently used to inform HRA model multipliers. In this paper, quantitative data from empirical studies regarding the effect of continuous hours of wakefulness on performance measures (reaction time, accuracy, and number of lapses) are used to develop a method to derive PSF multiplier values for sleep deprivation, in the context of the SPAR-H model. Data is extracted from the identified studies according to the meta-analysis research synthesis method and used to investigate performance trends and error probabilities. The error probabilities in test and control conditions are compared, and the resulting probability ratios are suggested for use in informing the selection of PSF multipliers in HRA methods. Although illustrated for sleep deprivation, the proposed methodology is general, and can be applied to other performance shaping factors.

Human Reliability Assessment under Uncertainty – Towards a Formal Method

Humans are and will remain one of the critical constituents of a technological system. The study of human factors is a broad domain with equally varying applications. Furthermore, with the advent of new technologies in safety-critical systems there is always a need to ensure system safety and reliability in accordance with increasingly demanding certification standards. Human reliability is a cause of concern as hardware becomes increasingly reliable and relatively human error is rising in its share of causing an accident. Human Reliability Analysis (HRA) provides a way to quantify the risk associated with a human. This paper presents a discussion on the development of a HRA model for the domain of transportation, rail transport in particular. Railway specific human factors studies are analyzed to identify safety relevant factors in order to create a relevant and relatively applicable Performance Shaping Factor list. This list of factors is compared with railway specific studies to address domain specific concerns, further augmenting it with quantification levels for each. A discussion on our proposition towards the integration of HRA for obtaining human induced system-level risk taking into account uncertainty in data and current work's positioning in proposed methodology is also included.

A Bayesian approach to treat expert-elicited probabilities in human reliability analysis model construction

In human reliability analysis (HRA), models are often used for the prediction of human error probabilities (HEPs), given a set of performance conditions, typically represented by ratings on a set of influencing factors. The relationships underlying these models (yielding HEPs for specific sets of factor ratings) should ideally be built on empirical data. However the derivation of these relationships in practice has to cope with limited availability of data, so that a strong component of expert judgment is always present. Nevertheless, the incorporation of expert judgment in HRA models is typically not done in a formal way, so that that it is often impossible to distinguish source data and judgments. In this context, this paper presents a Bayesian approach to aggregate expert estimates on human error probabilities to determine the relationships of an HRA model. The idea is to build a computable model using information from experts, provided as estimates. A numerical example demonstrates that the approach formally and transparently represents (and distinguishes) the inherent variability of the HEP quantity as well as that of the experts providing their estimates.

Deriving causal Bayesian networks from human reliability analysis data: A methodology and example model

Within the probabilistic risk assessment community, there is a widely acknowledged need to improve the scientific basis of human reliability analysis (HRA). This has resulted in a number of independent research efforts to gather empirical data to validate HRA methods and a number of independent research efforts to improve theoretical models of human performance used in HRA. This paper introduces a methodology for carefully combining multiple sources of empirical data with validated theoretical models to enhance both qualitative and quantitative HRA applications. The methodology uses a comprehensive set of performance influencing factors to combine data from different sources. Further, the paper describes how to use data to gather insights into the relationships among performance influencing factors and to build a quantitative HRA causal model. To illustrate how the methodology is applied, we introduce the Bayesian network model that resulted from applying the methodology to two sources of human performance data from nuclear power plant operations. The proposed model is introduced to demonstrate how to develop causal insights from HRA data and how to incorporate these insights into a quantitative HRA model. The methodology in this paper provides a path forward for carefully incorporating emerging sources of human performance data into an improved HRA method. The proposed model is a starting point for the next generation of data-informed, theoretically-validated HRA methods.

HRA in China: Model and data

Human reliability analysis (HRA) is generally viewed as quite an important part in probabilistic safety assessment (PSA). In this paper, a THERP + HCR HRA model is presented to model the operators’ post-accident behavior in Chinese nuclear power plants. The paper shows how the model is structured and how to consider and acquire the corresponding data, including HCR data modification and HRA event tree data. A case study is presented to make an illustration.

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.

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.

Advances in human reliability analysis methodology. Part II: PC-based HRA software

Abstract This paper, in two parts, summarizes some of the advancements made in the area of human reliability analysis (HRA) in the past decade. The paper focuses on the HRA program sponsored by the Electric Power Research Institute (EPRI) since 1982 as part of an effort to better understand the role of operators in safe operation of nuclear power plants (NPPs) and advance the state-of-the-art in HRA. Many technical reports have been published and numerous papers have been presented in national and international conferences on the various EPRI HRA projects. This paper is an attempt to summarize a decade of research in this area with an emphasis on recent advancements made towards development of a simulator-based HRA methodology using data from NPP simulators. HRA frameworks, models, data and computer codes are discussed, and areas for further research are pointed out. This paper discusses the PC-based software developed to facilitate the process of simulator data collection and analysis as well as the assessment of human reliability.

Advances in human reliability analysis methodology. Part I: frameworks, models and data

Abstract This paper, in two parts, summarizes some of the advancements made in the area of human reliability analysis (HRA) in the past decade. The paper focuses on the HRA program sponsored by the Electric Power Research Institute (EPRI) since 1982 as part of an effort to better understand the role of operators in safe operation of nuclear power plants (NPPs) and advance the state-of-the-art in HRA. Many technical reports have been published and numerous papers have been presented in national and international conferences on the various EPRI HRA projects. This paper is an attempt to summarize a decade of research in this area with an emphasis on recent advancements made towards development of a simulator-based HRA methodology using data from NPP simulators. HRA frameworks, models, data and computer codes are discussed, and areas for further research are pointed out. Part I herein covers the frameworks, models and data. Part II of the paper (see this issue, pp. 57–66) discusses the PC-based software developed to facilitate the process of simulator data collection and analysis as well as the assessment of human reliability.