A bibliometric analysis of human reliability research

Three severe nuclear power accidents (Fukushima accident, Chernobyl accident and Three Mile Island accident) have proved that human errors in the mitigation of severe accidents will exacerbate the deterioration of accidents and cause immeasurable consequences. Therefore, there is an urgent need for finding human reliability analysis methods to effectively analyze and predict possible human errors in severe accident mitigation.In order to compare the applicability of the Human Reliability Analysis (HRA) method under the severe accident in the nuclear power plant, this paper put forward four criteria, qualitative analysis, quantitative analysis, traceability and availability, based on the characteristics of the personnel in the process of severe accident mitigation. Several commonly used HRA methods were compared and analyzed. The results show that the common HRA methods which applied to the analysis have shortcomings, so it is necessary to establish a new HRA method, or to improve the original methods to solve the issues under severe accident conditions and the IDAC model and Phoenix HRA methodology are considered better choice. Combined with computer programs,the IDAC model can simulate the process of accident and the dynamic response of personnel, which helps to explain the dynamic interaction characteristics of human behavior and environment, but the model still has some limitations in the aspects of uncertainty analysis and simulation data availability. While Phoenix method provides some Performance influencing factors and crew failure modes that can reflect the response characteristics of emergency personnel. This applicability study can provide guidance for the establishment of HRA method in severe accidents.

The traditional approach to the study of human factors in the maritime field involves the analysis of accidents without considering human factors reliability analysis. The main approach being use to analyze human errors are statistical approach and probability theory approach. Another suitable approach to the study of human factors in the maritime industry is the quasi-experimental field study where variations in performance (for example attention) can be observed as a function of natural variations in performance shaping factors. This paper analyzes result of modeling human error and human reliability emanating from the use of technology on board ship navigation in coastal water area by using qualitative and quantitative tools. Accident reports from marine department are used as empirical material for quantitative analysis. The literature on safety is based on common themes of accidents, the influence of human error resulting from technology usage design, accident report from MAIB and interventions information are use for qualitative assessment. Human reliability assessment involves analysis of accident in waterways, emanating from human-technology factors interface. This paper report an enhancement requirement of the methodological issues with previous research study, monitoring and deduce recommendations for technology modification of the human factors necessary to improve maritime safety performance. The result presented can contribute to rule making, and safety management leading for development of guideline and standards for human reliability risk management for ship navigating within inland and coastal waters.

As manufacturers and technologies become more complicated, manufacturing errors such as machine failure and human error have also been considered more over the past. Since machines and humans are not error-proof, managing the machines and human errors is a significant challenge in manufacturing systems. There are numerous methods for investigating human errors, fatigue, and reliability that categorized under Human Reliability Analysis (HRA) methods. HRA methods use some qualitative factors named Performance Shaping Factors (PSFs) to estimate Human Error Probability (HEP). Since the PSFs can be considered as the acceleration factors in Accelerated Life Test (ALT). We developed a method for Accelerated Human Fatigue Test (AHFT) to calculate human fatigue, according to fatigue rate and other effective factors. The proposed method reduces the time and cost of human fatigue calculation. AHFT first extracts the important factors affecting human fatigue using Principal Component Analysis (PCA) and then uses the accelerated test to calculate the effect of PSFs on human fatigue. The proposed method has been applied to a real case, and the provided results show that human fatigue can be calculated more effectively using the proposed method.

Human reliability in railway engineering: Literature review and bibliometric analysis of the last two decades

A comparison of the quantification aspects of human reliability analysis methods in nuclear power plants

Human error in manual assembly systems affects system reliability, safety and it is one of the most important causes of quality defects. Many researchers have developed methods for Human Reliability Analysis (HRA) with the aim of identifying, modeling, quantifying and reducing human error, mainly in safety-critical industries. This paper addresses human reliability analysis in assembly systems, where most of the employed workforce is currently involved. The main purpose is to systematically investigate the current state-of-the-art on this topic, analyzing and summarizing the theoretical and empirical contents, identifying patterns and research streams, informing the strengths and weakness of selected literature and so highlighting the research and practice opportunities. The results of this study show that a prospective analysis of human reliability in the manual assembly systems until now has been neglected in literature. Nevertheless, HRA methods and assembly specific methodologies and approaches found in literature can be successfully applied to assembly systems, allowing users to predict human error probability and to determine the most significant error influencing factors. Furthermore, the results highlight the role of human error in the occurrence of quality defects (rejected or reworking). This paper, therefore, contributes to the transfer of knowledge about human reliability gained in manual assembly operations, providing practitioners and researchers with a comprehensive overview of this topic and several research opportunities for future studies.

<p>Human error in manual assembly systems affects system reliability, safety and it is one of the most important causes of quality defects. Many researchers have developed methods for Human Reliability Analysis (HRA) with the aim of identifying, modeling, quantifying and reducing human error, mainly in safety-critical industries. This paper addresses human reliability analysis in assembly systems, where most of the employed workforce is currently involved. The main purpose is to systematically investigate the current state-of-the-art on this topic, analyzing and summarizing the theoretical and empirical contents, identifying patterns and research streams, informing the strengths and weakness of selected literature and so highlighting the research and practice opportunities. The results of this study show that a prospective analysis of human reliability in the manual assembly systems until now has been neglected in literature. Nevertheless, HRA methods and assembly specific methodologies and approaches found in literature can be successfully applied to assembly systems, allowing users to predict human error probability and to determine the most significant error influencing factors. Furthermore, the results highlight the role of human error in the occurrence of quality defects (rejected or reworking). This paper, therefore, contributes to the transfer of knowledge about human reliability gained in manual assembly operations, providing practitioners and researchers with a comprehensive overview of this topic and several research opportunities for future studies. </p>

The human factors area is one of the core field of industrial safety. It is deeply recognized that human factors are the main cause of accidents and breakdowns in various industries. Physiological mental states of human operators, like fatigue or vigilance, are crucial for the evaluation of human operators reliability. However, the shortages of measurements of physiological mental states limit their application. So human reliability analysis (HRA) is developed and many techniques have been developed. Among these techniques, cognitive reliability and error analysis method (CREAM) is often applied and discussed. Due to the application limits of common performance conditions (CPCs) in original CREAM approach, it is advised to generate a new list of CPCs for the application domain if CREAM approach is applied to other domains. In this contribution, a new approach defining a situated and dynamical human reliability measure is established. The approach is based on the well-known CREAM approach, which is modified with respect to the use in dynamical context. The new list of CPCs, their levels and corresponding effects on human reliability are determined. A new index, human performance reliability score (HPRS), is proposed for the assessment of human operator reliability. Finally, the applicability and correctness of the newly established approach is verified by data analysis.

A Bayesian approach to comparing human reliability analysis methods using human performance data

Given the significant differences in the scope, approach, and underlying models of a relatively wide range of existing HRA methods, there has been a growing interest on the part of HRA method developers and users to empirically test the various methods. To this end, there is an ongoing international effort to begin this process by testing the application of HRA methods to nuclear power plant operating crew performance in the HAMMLAB simulators at the Halden Reactor Project in Norway. Initial efforts in designing and implementing these studies have identified a number of issues associated with structuring the studies in order to allow an adequate and appropriate test of the different methods. This paper focuses on issues associated with applying HRA methods in the context of an empirical study, particularly when a research simulator is used for data collection. Example issues include: determining the scope of the analysis when the methods themselves differ in the scope of the HRA processes they address, accounting for differences between the methods in how they use simulator exercises to support the analysis, addressing the impact of experimental controls on application of methods, and given the low probability of human failure events typically modelled in nuclear power plant probabilistic risk/safety assessments (PRAs/PSAs), the need for analysts to present their results in a somewhat different format than they usually do. These types of issues related to applying HRA methods in the context of empirical studies are discussed and resolutions are proposed.

The NRC recognizes that a more complete technical basis for understanding and regulating advanced digital technologies in commercial nuclear power plants is needed. A concern is that the introduction of digital safety systems may have an impact on risk. There is currently no standard methodology for measuring digital system reliability. A tool currently used to evaluate NPP risk in analog systems is the probabilistic risk assessment (PRA). The use of this tool to evaluate the digital system risk was considered to be a potential methodology for determining the risk. To test this hypothesis, it was decided to perform a limited PRA on a single dominant accident sequence. However, a review of existing human reliability analysis (HRA) methods showed that they were inadequate to analyze systems utilizing digital technology. A four step process was used to adapt existing HRA methodologies to digital environments and to develop new techniques. The HRA methods were then used to analyze an NPP that had undergone a backfit to digital technology in order to determine, as a first step, whether the methods were effective. The very small-break loss of coolant accident sequence was analyzed to determine whether the upgrade to the Eagle-21 process protection system had an effect on risk. The analysis of the very small-break LOCA documented in the Sequoyah PRA was used as the basis of the analysis. The analysis of the results of the HRA showed that the mean human error probabilities for the Eagle-21 PPS were slightly less than those for the analog system it replaced. One important observation from the analysis is that the operators have increased confidence steming from the better level of control provided by the digital system. The analysis of the PRA results, which included the human error component and the Eagle-21 PPS, disclosed that the reactor protection system had a higher failure rate than the analog system, although the difference was not statistically significant.

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

Human error is one of the most important factors contributing to occupational accidents. Human Reliability Analysis (HRA) methods have been used successfully in many fields to determine the probability of errors contributing to such accidents, estimate the impact of their consequences, and develop error reduction strategies. In this study, it has described some HRA methods and their implementations in several fields, including maritime, aviation, railway, space, health, nuclear, petrochemical, and construction sectors. Examples are drawn from articles dealing with HRA issues listed in the Web of Science Core Collection database between 2012 and 2022. The relatively small number of HRA studies conducted in Türkiye are found mainly in maritime applications. This review is intended to encourage the widespread use of HRA methods in all industries.

To support data collection for dynamic human reliability analysis (HRA), this study investigates time distributions for task primitives defined in the Goals, Operators, Methods, and Selection rules (GOMS)–Human Reliability Analysis (HRA) method and Human Reliability data EXtraction (HuREX). GOMS-HRA was developed to provide cognition-based time and human error probability (HEP) information for dynamic HRA calculations within the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) framework, while HuREX is a comprehensive HRA data collection method developed by the Korea Atomic Energy Research Institute (KAERI). In this paper, we examine time distributions by using experimental data collected from the Simplified Human Error Experimental Program (SHEEP) study, which proposes an HRA data collection framework to complement full-scope simulator research and gather input data for dynamic HRA by using simplified simulators such as the Rancor Microworld simulator. This paper investigates whether the time required for GOMS-HRA and HuREX task primitives fits 13 statistical distributions. Additionally, we compare and discuss the time distributions obtained from both student operators and professional operators. The result was that this study identified several time distributions for five GOMS-HRA and four HuREX task primitives. In the future, the results of this study are expected to provide objective reference data on the elapsed time for task primitives and aid in realistically simulating scenarios within dynamic HRA.

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.