Human reliability analysis (HRA) in the context of HRA testing with empirical data

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.

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

In probabilistic risk assessment (PRA) of nuclear power plants (NPPs), human reliability analysis (HRA) is conducted to identify potential human failure events that could contribute to risk scenarios and estimate human error probabilities. Lessons learned from the 2011 Fukushima Daiichi NPP accident underscored that for PRA, it is critical to model external control room (Ex-CR) human actions. The state-of-practice HRA methods, historically developed for the main control room HRA, are limited in capturing the unique nature of Ex-CR human actions, such as location dependence (in addition to the time dependence) of human actions and spatiotemporal interactions of human performance with the surrounding physical environments, for instance, hazard propagation. To advance the Ex-CR HRA in the context of the fire PRA for NPPs, the authors’ team developed a simulation-based fire crew performance model using an agent-based modeling (ABM) technique. The ABM fire crew simulation was coupled with a fire progression model through a spatiotemporal interface using a geographic information system. This paper focuses on the validation of the ABM simulation, which is the key requirement for the simulation-based Ex-CR human performance model to be utilized in PRA. The existing validation approach, initially developed for physical models in the fire PRA of NPPs, is extended for validation of the simulation-based Ex-CR human performance model. Model uncertainty is used as a measure of model validity, which facilitates the incorporation of the validation result into the PRA. The degree of the model uncertainty is characterized by a lognormal error model whose parameters are quantified based on a pairwise comparison between empirical data and model predictions. The proposed validation approach is demonstrated using a case study of the fire PRA of NPPs. This study makes two research contributions: (1) it is the first to validate the simulation-based Ex-CR human performance model against empirical human performance data and incorporate the validation result into the PRA of NPPs, and (2) this study, for the first time, conducts a controlled experimental test to collect empirical data for fire crew performance at NPPs.

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

Assessment of HRA method predictions against operating crew performance: Part II: Overall simulator data, HRA method predictions, and intra-method comparisons

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.

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.

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

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.

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.

The area of Human Reliability Analysis (HRA) has become increasingly important as both the nuclear industry and other high-technology industries have become conscious of the need for risk management and risk analysis. Techniques for performing and validating HRA should clearly be subjects of Human Factors Test and Evaluation attention. However, these issues have received less attention than they probably deserve from the Human Factors Society, despite the key contributions to the field of such HFS Test and Evaluation Group members as Dr. Alan Swain and Dr. David Meister. Issues that should be of interest to the HFS membership were recently raised in a white paper, recently published in “Reliability Engineering and System Safety” as an editorial. This editorial, titled “HRA — Where Shouldst Thou Turn?”, was written by Mr. Ed Dougherty, an HRA practitioner. The editorial takes to task (“pillories”, might not be too strong a term) almost every major technique and method for HRA currently in use, including his own. In addition, he castigates the US Nuclear Regulatory Commission attitudes and philosophies toward HRA use. Due to the controversial nature of the editorial, the journal editor, Dr. Apostolaikis, provided an opportunity for response to some fifteen practitioners/theorists of those mentioned. Some of these responders included such HFS members as Alan Swain, David Woods, Barry Kantowitz, Thomas Ryan, and Jens Rasmussen. However, these responders had no chance to review each others responses, nor has Mr. Dougherty had any opportunity to respond to the responses.

Dependency definitions for quantitative human reliability analysis

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

A bibliometric analysis of human reliability research

HRA studies in healthcare highlight that PSF (or Influencing Factors—IFs) taxonomies in HRA techniques have been developed and validated in industrial contexts, and as such are not fully applicable to healthcare contexts. In this paper, a modified version of Human Error Assessment and Reduction Technique (HEART), has been developed and tested through an application to the robotic surgical Radical Prostatectomy procedure. Personal and organizational factors were modeled and assessed through an IFs taxonomy validated in the surgical domain, and then systematically translated into the corresponding Error Producing Conditions (EPCs), typical of the HEART method. The results confirmed the importance of adapting HRA methods to the healthcare sector, and added detailed information on what are the most relevant factors that should be captured by an HRA method when applied to surgery. Additionally, the analysis revealed that team related factors have the highest influence on surgeons’ performance (i.e. increase of Human Unreliability Rate) in the context of different surgical tasks.