Dynamic Human Reliability Analysis Research Articles

Dynamic human reliability analysis method for nuclear power plant main control room based on SPAR-H and SD

Human reliability analysis (HRA) plays a vital role in probabilistic risk assessment (PRA) by quantifying the reliability and safety of complex technical systems through the estimation of human error probabilities (HEPs). Standardized plant analysis risk human (SPAR-H) reliability analysis is widely used for HRA; it adjusts the nominal HEP by assigning different multipliers to the performance shaping factors (PSFs). However, SPAR-H cannot capture the time-varying nature of HEPs; therefore, it cannot realistically respond to the accumulation and fluctuation of human risk factors across the mission. Hence, this study proposed a systematic method of dynamic HRA. It includes decision-making trial and evaluation laboratory-interpretative structural modeling (DEMATEL-ISM) to qualitatively and quantitatively analyze the hierarchical causality among the PSFs and their influence on each other based on their relative relationship in SPAR-H and construct hierarchical causality diagrams among the PSFs. Additionally, it uses a system dynamics method in conjunction with the analysis of time-dependent PSFs based on the hierarchical causality diagrams of PSFs to respond to the time-varying nature of HEPs. The simulation and rationale check results of the cases show that the proposed dynamic HRA method can realize a more realistic and dynamic estimation of HEPs.

Dynamic assessment method for human factor risk of manned deep submergence operation system based on SPAR-H and SD

Due to the complex and changeable contextual environment, the human factor risk of the manned deep submergence operating system shows dynamic characteristics. Compared with the traditional static human reliability analysis (HRA) method, dynamic HRA method can better simulate the dynamic characteristics and the nonlinear information feedback mechanism of the operating system. This paper proposed a dynamic risk assessment model based on System Dynamics and SPAR-H. The cognitive load was introduced into the Performance Shaping Factor (PSF) network to make it more suitable for the task and environment of manned deep submergence. In addition, in order to measure the compensation effect of PSF on the work efficiency of oceanauts, eight compensation functions were constructed between PSFs and work efficiency. Finally, key risk tasks and sensitive PSFs were identified. Taking the 12 h manned deep diving mission as an example, the dynamic quantification of human error probability and work efficiency of oceanauts was simulated. The results indicate that the dynamic simulation results of the constructed risk assessment model are consistent with the actual situation, and can effectively predict the changes of dynamic risk.

Modeling Task Time Duration Using the Hunter Dynamic Human Reliability Analysis FrameworK

The Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) is simulation software for modeling the human portion of risk. HUNTER is built on a framework that separately models individual, task, and environmental factors. As a dynamic human reliability analysis (HRA) method, it affords advantages over traditional static or worksheet-based HRA approaches, such as the ability to model what-if scenarios and produce outputs beyond human error probabilities. In this paper, a validation was conducted of HUNTER task time estimates vs. empirical data collected for a steam generator tube rupture scenario. The HUNTER model produced using GOMS-HRA task-level primitives yielded timing results very closely aligned to empirical reference data for base and complex conditions, suggesting the tool holds promise for HRA requiring time estimation.

A methodology for Dynamic Human Reliability Analysis in Robotic Surgery

Surgery has changed significantly in recent years due to the introduction of advanced technologies, resulting in increased system complexity at the technical, human and organisational levels, which may lead to higher variability of patient outcome due to new error pathways. Current approaches towards a safer surgery are largely based on ex-post analysis of events and process monitoring (e.g. root cause analysis, safety checklists, safety audits). However, adopting a proactive approach enables the prior identification of critical factors and the design of safer sociotechnical systems, thanks to a multi-level (or mesoergnomics) perspective. In this paper, a methodology for performing mesoergonomics analysis of surgical procedures is proposed. It is a methodology for Dynamic Human Reliability Analysis in Robotic Surgery based on a modified version of human error assessment and reduction technique (HEART) integrated with a method for incorporating uncertainties related to the influence of personal and organisational factors on the execution of a surgical procedure. The pilot application involves a robot-assisted radical prostatectomy procedure, and the results reveal that team-related factors have the greatest impact on patient outcome variability.

Development of a quantitative Bayesian network mapping objective factors to subjective performance shaping factor evaluations: An example using student operators in a digital nuclear power plant simulator

Traditional human reliability analysis methods consist of two main steps: assigning values for performance shaping factors (PSFs), and assessing human error probability (HEP) based on PSF values. Both steps rely on expert judgment. Considerable advances have been made in reducing reliance on expert judgment for HEP assessment by incorporating human performance data from various sources (e.g., simulator experiments); however, little has been done to reduce reliance on expert judgment for PSF assignment. This paper introduces a data-driven approach for assessing PSFs in Nuclear Power Plants (NPPs) based on contextual information. The research illustrates how to develop a Bayesian PSF network using data collected from student operators in a NPP simulator. The approach starts with a baseline PSF model that calculates PSF values from context information during an accident scenario. Then, a Bayesian model is developed to link the baseline model to the Subjective PSFs. Two additional factors are included: simulator bias and context information. Results and analysis include variation between the results of the proposed model and the training dataset, and the significance of each element in the model. The proposed approach reduces the reliance of PSF assignment on expert judgment and is particularly suitable for dynamic human reliability analysis.

Human reliability research of numerical control machining based on optimized Holistic Decision Tree

Human reliability has been a focus in system reliability and safety researches since 1960s, and now human factor has become an increasingly important reason which affects system reliability. Numerical control machining system characterized by man-machine interaction can be applied to automatic parts machining with high efficiency. However, professional and technical staffs are still needed for clamping, positioning, inspection and others. So there is no doubt that the quality of the parts might be influenced by personnel behaviour. What’s more, on-site staff is responsible for handling potential fault directly. The study on human error plays an important role to ensure the quality and reduce industrial accident. Holistic Decision Tree (HDT) is a dynamic Human Reliability Analysis (HRA) method, emphasizing the wholeness of man-machine interaction. This paper tries to use an optimized HDT method to investigate human reliability in numerical control machining system. In order to obtain a rational order of the importance of each IF, this paper incorporates entropy weight method to the conventional expert method to determine the relative weight value of each human behaviour influence factor (IF), and finally calculates the human error probability (HEP).

Testing Subtask Quantification Assumptions for Dynamic Human Reliability Analysis in the SPAR-H Method

Human reliability analysis (HRA) to date has relied almost entirely on static methods. To provide a more precise model of human performance, dynamic HRA has been developed. Dynamic HRA must model a range of human actions typically at a finer resolution than is accounted for by overall tasks or human failure events (HFEs) used by static HRA. Parsing HFEs into finer units of analysis requires consideration of the mathematical underpinnings of the HRA methods that will be used for quantification. This paper serves to test how conventional HRA methods scale to this level of precision. SPAR-H, a static HRA method, was evaluated as part of this research. SPAR-H, which is based on HFEs, may require further refinement before its quantification approach can be employed in dynamic HRA.

The Development of Dynamic Human Reliability Analysis Simulations for Inclusion in Risk Informed Safety Margin Characterization Frameworks

The United States Department of Energy is sponsoring the Light Water Reactor Sustainability program, which has the overall objective of supporting the near-term and the extended operation of commercial nuclear power plants. One key research and development (R&D) area in this program is the Risk-Informed Safety Margin Characterization pathway, which combines probabilistic risk simulation with thermohydraulic simulation codes to define and manage safety margins. The R&D efforts to date, however, have not included robust simulations of human operators, and how the reliability of human performance or lack thereof (i.e., human errors) can affect risk-margins and plant performance. This paper describes current and planned research efforts to address the absence of robust human reliability simulations and thereby increase the fidelity of simulated accident scenarios.