Application of accelerated life testing in human reliability analysis

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.

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

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 transportation of steel scrap cargoes in global trade has been increasing over the years. According to the researches, the demand of steel scrap materials will be more than double in the late 21st century whereby forecasting of steel scrap production and so steel scrap cargo operations will also evenly rise in ports. Increasing steel scrap cargo operations will bring along several undesirable accidents and injuries. Investigations show that the most paramount reason for these incidents or accidents related with steel scrap cargo operations in maritime sector is human error. In this sense, it is aimed to identify human error probabilities (HEPs) for steel scrap cargo operation that is performed frequently in maritime sector, especially in bulk carrier vessels. In this study, Success Likelihood Index Method (SLIM), which is one of the methods for Human Reliability Analysis (HRA), is used to determine HEPs in steel scrap cargo operations due to the limited data availability on this topic. Accordingly, the most common error modes that are determined via detailed literature review are ranked in accordance with HEP values. It is put forward which and how much error mode is affected from Performance Shaping Factor (PSF) such as education, supervision, environmental condition, equipment and tool condition, and experience mostly. According to the analysis results, “the falling piece of steel scrap on the deck during steel scrap loading or unloading operation” has the most probability occurrence. Consequently, it is understood that training and experience factors are critically important for preventing errors in steel scrap cargo operation in overall view. On the other hand, environmental condition, supervision, and equipment and tool condition factors include the prominent level of significance to bring down the probability of accruing of some specific errors. Accordingly, the proposed approach not only make a theory-based contribution to the maritime literature, but also to active contribution to the sector involving P&I Clubs, shipping companies, and classification societies toward focusing point for minimizing the accidents about steel scrap cargo operations.

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.

Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

Quantification of performance shaping factors (PSFs)’ weightings for human reliability analysis (HRA) of low power and shutdown (LPSD) operations

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.

A method for assessing Human Error Probability through physiological and psychological factors tests based on CREAM and its applications

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.

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>

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

A bibliometric analysis of human reliability research

Humans are an integral part of complex systems such as nuclear power plants and have to play a significant role in ensuring the safety and reliability of these systems. Failure to perform the intended task within the stipulated time by the operator can challenge the safety of the system. Human reliability analysis (HRA) is a widely practiced methodology to estimate the contribution of operator error towards the overall risk to the facility. HRA methods quantify this contribution in terms of human error probability (HEP) accounting for various psychological and physiological factors that influence the performance of the operator. These factors are referred to as human factors (HF), which enhance or degrade the human performance. The paper discusses the use of virtual simulation as a tool to generate the HF data from the virtual model of an in-house experimental facility. This paper also demonstrates the use of multi-attribute utility theory to determine a suitable HRA method amongst several HRA methods to quantify the HEP based on the desired set of HRA attributes. As classical HRA methods, generally, do not address the interactions among the HFs, the Bayesian network technique has been employed in this study to account for HF interactions.