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

In the probabilistic risk assessments (PRAs) used in the nuclear industry, human failure events (HFEs) are determined as a subset of hardware failures, namely those hardware failures that could be triggered by human action or inaction. This approach is top-down, starting with hardware faults and deducing human contributions to those faults. Elsewhere, more traditionally human factors driven approaches would tend to look at opportunities for human errors first in a task analysis and then identify which of those errors is risk significant. The intersection of top-down and bottom-up approaches to defining HFEs has not been carefully studied. Ideally, both approaches should arrive at the same set of HFEs. This question is crucial, however, as human reliability analysis (HRA) methods are generalized to new domains like oil and gas. The HFEs used in nuclear PRAs tend to be top-down—defined as a subset of the PRA—whereas the HFEs used in petroleum quantitative risk assessments (QRAs) often tend to be bottom-up—derived from a task analysis conducted by human factors experts. The marriage of these approaches is necessary in order to ensure that HRA methods developed for top-down HFEs are also sufficient for bottom-up applications.

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.

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

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

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.

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.

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.

Human reliability analysis—why not turn to the human factors community?

A bibliometric analysis of human reliability research

This report describes the development plan for verification and validation (V&V) of multihazard risk informed margin management (RIMM) methods and tools. The plan will include using new INL developed tool sets coupled with the new multi-partner External Hazards Experimental Group (EHEG) coordinated by the Idaho National Laboratory (INL) within the Risk-Informed Safety Margin Characterization (RISMC) technical pathway of the Light Water Reactor Sustainability Program. The RISMC pathway is developing a suite of new tools and methods for advanced evaluation of facility risk. The RISMC toolkit, includes tools and methods focused on evaluation of risk from external hazards (e.g, seismic and flooding events), which have been shown to be dominant risk contributors in probabilistic risk assessments performed for operating nuclear power plants (NPPs). The external events activity (EEA) within the RISMC pathway is tasked with developing tools and methods focused on evaluation of multi-hazard external risk (e.g, seismic and flooding events). The EHEG will provide technical expertise and experimental large-scale testing data needed for development and validation of tools and methods in the RISMC toolkit for external hazard safety evaluations. The EEA within RISMC has two key elements: (1) an organizational and research framework provided by INL and (2) a coordinated group of university and national laboratory partners with the complementary expertise and experimental capabilities needed to conduct large-scale external hazard-focused experiments. This cooperative group will allow INL to leverage a range of existing capabilities to meet the unique needs of RISMC tool and method development. In addition, the capabilities of the EHEG could be used to address needs of other national laboratories. Currently, there is limited data available for development and validation of the tools and methods being developed in the RISMC Toolkit. The EHEG is being developed to obtain high quality, large-scale experimental data validation of RISMC tools and methods in a timely and cost-effective way. The group of universities and national laboratories that will eventually form the EHEG (which is ultimately expected to include both the initial participants and other universities and national laboratories that have been identified) have the expertise and experimental capabilities needed to both obtain and compile existing data archives and perform additional seismic and flooding experiments. The data developed by EHEG will be stored in databases within RISMC. These databases will be used to validate the advanced external hazard tools and methods. The numerical tools under development in RISMC are used to evaluate the impacts that external hazards have on nuclear power plants. These analysis tools will be used to advanced current risk calculation processes and reduce uncertainty in these calculations. To have confidence in the predictive capability of these numerical tools it is important to verify and validate them. To verify and validate numerical tools the user must understand what physics is being used to represent the problem (i.e. constitutive models used to represent the soil and structure), the numerical solver approach used (i.e. finite element), what data already exists that could be used to validate the tools, and what data is needed for validation.

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

The IDA cognitive model for the analysis of nuclear power plant operator response under accident conditions. Part I: problem solving and decision making model

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.

A human’s knowledge base is a key component for the development of a mechanistic model of human response to be used for human reliability analysis. This paper proposes a new method for constructing this knowledge base. The proposed method is comprised of three steps: (1) systematic literature review, which is used to collect data pertinent to the subject under study; (2) summarization, the goal of which is to extract key points that are expressed in the literature; (3) qualitative coding, a process in which codes closely related to the topic are derived and the relationships between these codes are expressed. As a case study, the proposed method is being applied to construct an operator’s knowledge base concerning severe accident phenomenology in a nuclear power plant. Part of this application is explored in this paper. With the proposed method and the resulting knowledge base, it is expected that an individual’s response when presented with a specific context can be modeled in more detail.

Dependency definitions for quantitative human reliability analysis