A Framework to Assess Human Performance in Normal and Emergency Situations

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Salman Ahmed ◽  
H. Onan Demirel
Keyword(s):  
Computer Aided Design ◽  
Nuclear Power ◽  
Human Error ◽  
Human Performance ◽  
Plant Management ◽  
Early Design ◽  
Emergency Situations ◽  
Design Deficiencies ◽  
Emergency Scenarios ◽  
Digital Human

Abstract Human error is one of the primary reasons for accidents in complex industries like aviation, nuclear power plant management, and health care. Physical and cognitive workload, flawed information processing, and poor decision making are some of the reasons that make humans vulnerable to error and lead to failures and accidents. In many accidents and failures, oftentimes, vulnerabilities that are embedded in the system, in the form of design deficiencies and poor human factors, lead to latent or catastrophic failures, but the last link is a human operator who gets blamed or worse, injured. This paper introduces an early design human performance assessment framework to identify what type of digital prototyping methodologies are appropriate to detect the deviation of the operator's performance due to an emergency condition. Fire in a civilian aircraft cockpit was introduced as a performance shaping factor (PSF). Ergonomics performance was evaluated using two prototyping strategies: (1) a computational prototyping framework includes digital human modeling (DHM) and computer-aided design; and (2) a novel mixed prototyping framework includes motion capture, DHM, and virtual reality. Results showed that the mixed prototyping framework can simulate emergency scenarios with increased realism and also has the potential to incorporate subjective aspects of ergonomics outcomes, overcoming the underlying lack of design knowledge in conventional early design methodologies.

Using Simulator Data to Facilitate Human Reliability Analysis

2019 ◽  
Vol 141 (2) ◽  
Author(s):  
Mashrura Musharraf ◽  
Allison Moyle ◽  
Faisal Khan ◽  
Brian Veitch
Keyword(s):  
Reliability Analysis ◽  
Bayesian Methods ◽  
Human Error ◽  
Human Performance ◽  
Objective Assessment ◽  
Performance Data ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Emergency Situations ◽  
Emergency Scenarios

Data scarcity has always been a significant challenge in the domain of human reliability analysis (HRA). The advancement of simulation technologies provides opportunities to collect human performance data that can facilitate both the development and validation paradigms of HRA. The potential of simulator data to improve HRA can be tapped through the use of advanced machine learning tools like Bayesian methods. Except for Bayesian networks, Bayesian methods have not been widely used in the HRA community. This paper uses a Bayesian method to enhance human error probability (HEP) assessment in offshore emergency situations using data generated in a simulator. Assessment begins by using constrained noninformative priors to define the HEPs in emergency situations. An experiment is then conducted in a simulator to collect human performance data in a set of emergency scenarios. Data collected during the experiment are used to update the priors and obtain informed posteriors. Use of the informed posteriors enables better understanding of the performance, and a more reliable and objective assessment of human reliability, compared to traditional assessment using expert judgment.

Using Simulator Data to Facilitate Human Reliability Analysis in Offshore Emergency Situations

2018 ◽  
Author(s):  
Mashrura Musharraf ◽  
Allison Moyle ◽  
Faisal Khan ◽  
Brian Veitch
Keyword(s):  
Reliability Analysis ◽  
Bayesian Methods ◽  
Human Error ◽  
Human Performance ◽  
Objective Assessment ◽  
Performance Data ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Emergency Situations ◽  
Emergency Scenarios

Data scarcity has always been a significant challenge in the domain of human reliability analysis (HRA). The advancement of simulation technologies provides opportunities to collect human performance data that can facilitate both the development and validation paradigms of HRA. The potential of simulator data to improve HRA can be tapped through the use of advanced machine learning tools like Bayesian methods. Except for Bayesian networks, Bayesian methods have not been widely used in the HRA community. This paper uses a Bayesian method to enhance human error probability (HEP) assessment in offshore emergency situations using data generated in a simulator. Assessment begins by using constrained non-informative priors to define the HEPs in emergency situations. An experiment is then conducted in a simulator to collect human performance data in a set of emergency scenarios. Data collected during the experiment is used to update the priors and obtain informed posteriors. Use of the informed posteriors enable better understanding of the performance, and a more reliable and objective assessment of human reliability, compared to traditional assessment using expert judgment.

Based on Human Behavior Process of Human Error Defensive Management Research for NPP

2017 ◽  
Author(s):  
Shen Yang ◽  
Geng Bo ◽  
Li Dan
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Control Chart ◽  
Human Behavior ◽  
Nuclear Power ◽  
Human Error ◽  
Human Performance ◽  
Human Factor ◽  
Error Management ◽  
Root Cause

According to the research of nuclear power plant human error management, it is found that the traditional human error management are mainly based on the result of human behavior, the event as the point cut of management, there are some drawbacks. In this paper, based on the concept of the human performance management, establish the defensive human error management model, the innovation point is human behavior as the point cut, to reduce the human errors and accomplish a nip in the bud. Based on the model, on the one hand, combined with observation and coach card, to strengthen the human behavior standards expected while acquiring structured behavior data from the nuclear power plant production process; on the other hand, combined with root cause analysis method, obtained structured behavior data from the human factor event, thus forming a human behavior database that show the human performance state picture. According to the data of human behavior, by taking quantitative trending analysis method, the P control chart of observation item and the C control chart of human factor event is set up by Shewhart control chart, to achieve real-time monitoring of the process and result of behavior. At the same time, development Key Performance Indicators timely detection of the worsening trend of human behavior and organizational management. For the human behavior deviation and management issues, carry out the root cause analysis, to take appropriate corrective action or management improvement measures, so as to realize the defense of human error, reduce human factor event probability and improve the performance level of nuclear power plant.

Application of the Saint Code in Modelling and Analyzing Some Human Tasks in Nuclear Power Plants

1981 ◽  
Vol 25 (1) ◽  
pp. 105-109
Author(s):  
A. Mohsen M. Metwally ◽  
Zeinab A. Sabri ◽  
S. Keith Adams ◽  
Abdo A. Husseiny
Keyword(s):  
Task Analysis ◽  
Nuclear Power ◽  
Power Plants ◽  
Human Error ◽  
Nuclear Power Plants ◽  
Human Performance ◽  
Human Engineering ◽  
Performance Shaping Factors ◽  
Power Plant Operation ◽  
Error Probabilities

Two techniques useful for the simulation and analysis of human performance in tasks involving nuclear power plant operation, maintenance and testing are evaluated. The SAINT and THERP techniques are compared with respect to their relevance to conducting task analysis, estimation of human error probabilities and accounting for performance shaping factors in nuclear power plants. The results show that the SAINT is more flexible and has promising features for human engineering studies of complex systems when compared to the static THERP technique currently used in nuclear safety analysis.

Three Mile Island—A Year & a Half Later

1980 ◽  
Vol 24 (1) ◽  
pp. 285-285
Author(s):  
Donald E. Parr
Keyword(s):  
Human Factors ◽  
Nuclear Power ◽  
Human Error ◽  
Human Performance ◽  
Lessons Learned ◽  
Nuclear Industry ◽  
Short And Long Term ◽  
Definition Of ◽  
Industry Planning ◽  
The Military

Following last year's Three Mile Island (TMI) Accident, there remains much concern about what is being done to prevent future incidents. With obvious emphasis on the major role played by human error, the human factors community, some members of the nuclear industry, and even the general public, saw possible implications for human factors applications in the nuclear power industry. What was needed was a rational definition of possible human performance contributions to the accident, a carefully thoughtout plan for both short and long term improvements, and then everyone pitching in to help make an already safe and efficient industry even better. A unique opportunity existed to emphasize human factors contributions to system performance while taking advantage of many applicable “lessons learned” in aerospace and the military. What occurred over the eighteen months since TMI was a mixture of confused responses rivaling Abbott& Costello's famed “Who's on first?!” routine. Human factors specialists, snake oil salesmen, and many inexperienced but eager individuals and companies rushed headlong into the nuclear age with threats, promises, and simple solutions to “save the industry.” However, even before TMI, some useful activities were underway and others have been planned and pursued since. This paper provides an overall summary of human factors related activities including industry planning, active contract activities, studies, research, and a comprehensive bibliography.

Digital Human Modeling for Workspace Design

2008 ◽  
Vol 4 (1) ◽  
pp. 41-74 ◽  
Author(s):  
Don B. Chaffin
Keyword(s):  
Computer Aided Design ◽  
Human Performance ◽  
Digital Human Modeling ◽  
Cad Systems ◽  
Work Settings ◽  
Biomechanical Models ◽  
Human Modeling ◽  
Digital Human Models ◽  
Manual Tasks ◽  
Digital Human

Digital human modeling (DHM) technology offers human factors/ergonomics specialists the promise of an efficient means to simulate a large variety of ergonomics issues early in the design of products and manufacturing workstations. It rests on the premise that most products and manufacturing work settings are specified and designed by using sophisticated computer-aided design (CAD) systems. By integrating a computer-rendered avatar (or hominoid) and the CAD-rendered graphics of a prospective workspace, one can simulate issues regarding who can fit, reach, see, manipulate, and so on. In this chapter, I briefly describe the development of various DHM methods to improve CAD systems. Past concerns about early DHM methods are discussed, followed by a description of some of the recent major developments that represent attempts by various groups to address the early concerns. In this latter context, methods are described for using anthropometric databases to ensure that population shape and size are well modeled. Efforts to integrate various biomechanical models into DHM systems also are described, followed by a section that outlines how human motions are being modeled in different DHM systems. In a final section, I discuss recent work to merge cognitive models of human performance with DHM models of manual tasks. Much has been accomplished in recent years to make digital human models more useful and effective in resolving ergonomics issues during the design of products and manufacturing processes, but much remains to be learned and applied in this rapidly evolving aspect of ergonomics.

scholarly journals An Approach to Analyze Diagnosis Errors in Advanced Main Control Room Operations Using the Cause-Based Decision Tree Method

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3832
Author(s):  
Awwal Mohammed Arigi ◽  
Gayoung Park ◽  
Jonghyun Kim
Keyword(s):  
Decision Tree ◽  
Nuclear Power ◽  
Power Plants ◽  
Human Error ◽  
Cognitive Activity ◽  
Emergency Operation ◽  
Nuclear Industry ◽  
Working Environment ◽  
Control Rooms ◽  
Diagnosis Errors

Advancements in the nuclear industry have led to the development of fully digitized main control rooms (MCRs)—often termed advanced MCRs—for newly built nuclear power plants (NPPs). Diagnosis is a major part of the cognitive activity in NPP MCRs. Advanced MCRs are expected to improve the working environment and reduce human error, especially during the diagnosis of unexpected scenarios. However, with the introduction of new types of tasks and errors by digital MCRs, a new method to analyze the diagnosis errors in these new types of MCRs is required. Task analysis for operator diagnosis in an advanced MCR based on emergency operation was performed to determine the error modes. The cause-based decision tree (CBDT) method—originally developed for analog control rooms—was then revised to a modified CBDT (MCBDT) based on the error mode categorizations. This work examines the possible adoption of the MCBDT method for the evaluation of diagnosis errors in advanced MCRs. We have also provided examples of the application of the proposed method to some common human failure events in emergency operations. The results show that with some modifications of the CBDT method, the human reliability in advanced MCRs can be reasonably estimated.

Quantitative resilience evaluation on recovery from emergency situations in nuclear power plants

2021 ◽  
Vol 156 ◽  
pp. 108220
Author(s):  
Ji Tae Kim ◽  
Jonghyun Kim ◽  
Poong Hyun Seong ◽  
Jooyoung Park
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Emergency Situations

Design, calibration, and application of an airborne gamma spectrometer system in Switzerland

Geophysics ◽  
1997 ◽  
Vol 62 (5) ◽  
pp. 1369-1378 ◽  
Author(s):  
Georg F. Schwarz ◽  
Ladislaus Rybach ◽  
Emile E. Klingelé
Keyword(s):  
Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
Gamma Ray ◽  
Point Sources ◽  
Swiss Alps ◽  
Artificial Radioactivity ◽  
Radiation Level ◽  
Satellite Navigation System ◽  
Emergency Situations

Airborne radiometric surveys are finding increasingly wider applications in environmental mapping and monitoring. They are the most efficient tool to delimit surface contamination and to locate lost radioactive sources. To secure radiometric capability in survey and emergency situations, a new sensitive airborne system has been built that includes an airborne spectrometer with 256 channels and a sodium iodide detector with a total volume of 16.8 liters. A rack mounted PC with memory cards is used for data acquisition, with a GPS satellite navigation system for positioning. The system was calibrated with point sources using a mathematical correction to take into account the effects of gamma‐ray scattering in the ground and in the atmosphere. The calibration was complemented by high precision ground gamma spectrometry and laboratory measurements on rock samples. In Switzerland, two major research programs make use of the capabilities of airborne radiometric measurements. The first one concerns nuclear power plant monitoring. The five Swiss nuclear installations (four power plants and one research facility) and the surrounding regions of each site are surveyed annually. The project goal is to monitor the dose‐rate distribution and to provide a documented baseline database. The measurements show that all sites (with the exception of the Gösgen power plant) can be identified clearly on the maps. No artificial radioactivity that could not be explained by the Chernobyl release or earlier nuclear weapons tests was detected outside of the fenced sites of the nuclear installations. The second program aims at a better evaluation of the natural radiation level in Switzerland. The survey focused on the crystalline rocks of the Central Massifs of the Swiss Alps because of their relatively high natural radioactivity and lithological variability.

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