• Safety and Health at Work
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• v.9 no.1
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• pp.42-52
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• 2018

Background: Maintenance operations on-board ships are highly demanding. Maintenance operations are intensive activities requiring high man-machine interactions in challenging and evolving conditions. The evolving conditions are weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress. For example, extreme weather condition affects seafarers' performance, increasing the chances of error, and, consequently, can cause injuries or fatalities to personnel. An effective human error probability model is required to better manage maintenance on-board ships. The developed model would assist in developing and maintaining effective risk management protocols. Thus, the objective of this study is to develop a human error probability model considering various internal and external factors affecting seafarers' performance. Methods: The human error probability model is developed using probability theory applied to Bayesian network. The model is tested using the data received through the developed questionnaire survey of >200 experienced seafarers with >5 years of experience. The model developed in this study is used to find out the reliability of human performance on particular maintenance activities. Results: The developed methodology is tested on the maintenance of marine engine's cooling water pump for engine department and anchor windlass for deck department. In the considered case studies, human error probabilities are estimated in various scenarios and the results are compared between the scenarios and the different seafarer categories. The results of the case studies for both departments are also compared. Conclusion: The developed model is effective in assessing human error probabilities. These probabilities would get dynamically updated as and when new information is available on changes in either internal (i.e., training, experience, and fatigue) or external (i.e., environmental and operational conditions such as weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress) factors.

• Journal of the Korean Society of Safety
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• v.21 no.3 s.75
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• pp.137-142
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• 2006

Quantification of error possibility, in an HRA process, should be performed so that the result of the qualitative analysis can be utilized in other areas in conjunction with overall safety estimation results. And also, the quantification is an essential process to analyze the error possibility in detail and to obtain countermeasures for the errors through screening procedures. In previous studies for the quantification of error possibility, nominal values were assigned by the experts' judgements and utilized as corresponding probabilities. The values assigned by experts' experiences and judgements, however, require verifications on their reliability. In this study, the validity of new error possibility values in new MCR design was verified by using the Onisawa's model which utilizes fuzzy linguistic values to estimate human error probabilities. With the model of error probabilities are represented as analyst's estimations and natural language expression instead of numerical values. As results, the experts' estimation values about error probabilities are well agreed to the existing error probability estimation model. Thus, it was concluded that the occurrence probabilities of errors derived from the human error analysis process can be assessed by nominal values suggested in the previous studies. It is also expected that our analysis method can supplement the conventional HRA method because the nominal values are based on the consideration of various influencing factors such as PSFs.

• Journal of the Korean Society of Safety
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• v.22 no.1 s.79
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• pp.61-66
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• 2007

Car crashes are the leading cause of death for persons of every age. Specially, human-related factor has been known to be the primary causal factor of such crashes than vehicle-and environmental-related factors. There are various studies to analyze driver's behavior and characteristics in driving for reducing the car crashes in many areas of car engineering, psychology, human factor, etc. However, there are almost no studies which analyze mainly the human errors in driving and estimate their probabilities in terms of human reliability analysis. This study estimates the probability of human error in driving, i.e. driver error probability. First, fifty driver errors are investigated through DBQ (Driver Behavior Questionnaire) revision and the error likelihoods in driving are collected which are judged by skillful drivers using revised DBQ. Next, these likelihoods are converted into driver error probabilities using the results that verbal probabilistic expressions are changed into quantitative probabilities. Using these probabilities we can improve the warning effects on drivers by indicating their driving error likelihoods quantitatively. We can also expect the reduction effects of car accident through controlling especially dangerous error groups which have higher probabilities. Like these, the results of this study can be used as the primary materials of safety education on drivers.

• Journal of the Korean Institute of Gas
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• v.6 no.4 s.18
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• pp.1-7
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• 2002

Human errors can take place in all levels that include the design, production, construction, operation and maintenance of plant facilities. It was found that the causes were concerned with the effects of human error. This study verified characteristics of the on-site operators and error mechanism, and used the classifying sheet to analyze human error that occurred in process. Also, by applying the ASEP(Accident Sequence Evaluation Program) HRA(Human Reliability Analysis) procedure, the algorithm to estimate the HEP and the ASEP HEP program to analyze human error in the plant were developed. If it is built in on-site, possible human error incident will be prevented and the systematic human error prevention strategy will be devised.

• Safety and Health at Work
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• v.7 no.1
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• pp.6-11
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• 2016

Background: A permit to work (PTW) is a formal written system to control certain types of work which are identified as potentially hazardous. However, human error in PTW processes can lead to an accident. Methods: This cross-sectional, descriptive study was conducted to estimate the probability of human errors in PTWprocesses in a chemical plant in Iran. In the first stage, through interviewing the personnel and studying the procedure in the plant, the PTW process was analyzed using the hierarchical task analysis technique. In doing so, PTWwas considered as a goal and detailed tasks to achieve the goal were analyzed. In the next step, the standardized plant analysis risk-human (SPAR-H) reliability analysis method was applied for estimation of human error probability. Results: The mean probability of human error in the PTW system was estimated to be 0.11. The highest probability of human error in the PTW process was related to flammable gas testing (50.7%). Conclusion: The SPAR-H method applied in this study could analyze and quantify the potential human errors and extract the required measures for reducing the error probabilities in PTW system. Some suggestions to reduce the likelihood of errors, especially in the field of modifying the performance shaping factors and dependencies among tasks are provided.

• Nuclear Engineering and Technology
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• v.23 no.2
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• pp.174-182
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• 1991

Effects of human error relevant to the periodic test are incorporated in the evaluations of the unavailability and optimal test interval of a safety system. Two types of possible human error with respect to the test and maintenance are considered. One is the possibility that a good safety system is inadvertently left in a bad state after test（Type A human error) and the other is the possibility that a bad safety system is undetected upon the test（Type B human error). An event tree model is developed for the steady-state unavailability of a safety system in order to determine the effects of human errors on the system unavailability and the optimal test interval. A reliability analysis of the Safety Injection System (SIS) was peformed to evaluate the effects of human error on the SIS unavailability. Results of various sensitivity analyses show that ; (1) the steady-state unavailability of the safety system increases as the probabilities of both types of human error increase and it is far more sensitive to Type A human error, (2) the optimal test interval increases slightly as the probability of Type A human error increases but it decreases as the probability of Type B human error increases, and (3) provided that the test interval of the safety injction pump is kept unchanged, the unavailability of SIS increases significantly as the probability of Type A human error increases but slightly as the probability of Type B human error increases. Therefore, to obtain the realistic result of reliability analysis, one should take shorter test interval (not optimal test interval) so that the unavailability of SIS can be maintained at the same level irrespective of human error. Since Type A human error during test & maintenance influeces greatly on the system unavailability, special efforts to reduce the possibility of Type A human error are essential in the course of test & maintenance.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.2
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• pp.14-24
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• 2021

This study aims to examine probable human errors when landing an airplane by the use of SHERPA(systematic human error reduction and prediction approach) and propose methods for preventing the predictive human errors. It has been reported that human errors are concerned with a lot of accidents or incidents of an airplane. It is significant to predict presumable human errors, particularly in the operation mode of human-automation interaction, and attempt to reduce the likelihood of predicted human error. By referring to task procedures and interviewing domain experts, we analyzed airplane landing task by using HTA(hierarchical task analysis) method. In total, 6 sub-tasks and 19 operations were identified from the task analysis. SHERPA method was used for predicting probable human error types for each task. As a result, we identified 31 human errors and predicted their occurrence probability and criticality. Based on them, we suggested a set of methods for minimizing the probability of the predicted human errors. From this study, it can be said that SHERPA can be effectively used for predicting probable human error types in the context of human-automation interaction needed for navigating an airplane.

• Nuclear Engineering and Technology
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• v.45 no.2
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• pp.151-164
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• 2013

A key input for the assessment of Human Error Probabilities (HEPs) with Human Reliability Analysis (HRA) methods is the evaluation of the factors influencing the human performance (often referred to as Performance Shaping Factors, PSFs). In general, the definition of these factors and the supporting guidance are such that their evaluation involves significant subjectivity. This affects the repeatability of HRA results as well as the collection of HRA data for model construction and verification. In this context, the present paper considers the TAsk COMplexity (TACOM) measure, developed by one of the authors to quantify the complexity of procedure-guided tasks (by the operating crew of nuclear power plants in emergency situations), and evaluates its use to represent (objectively and quantitatively) task complexity issues relevant to HRA methods. In particular, TACOM scores are calculated for five Human Failure Events (HFEs) for which empirical evidence on the HEPs (albeit with large uncertainty) and influencing factors are available - from the International HRA Empirical Study. The empirical evaluation has shown promising results. The TACOM score increases as the empirical HEP of the selected HFEs increases. Except for one case, TACOM scores are well distinguished if related to different difficulty categories (e.g., "easy" vs. "somewhat difficult"), while values corresponding to tasks within the same category are very close. Despite some important limitations related to the small number of HFEs investigated and the large uncertainty in their HEPs, this paper presents one of few attempts to empirically study the effect of a performance shaping factor on the human error probability. This type of study is important to enhance the empirical basis of HRA methods, to make sure that 1) the definitions of the PSFs cover the influences important for HRA (i.e., influencing the error probability), and 2) the quantitative relationships among PSFs and error probability are adequately represented.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2037-2047
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• 2022

The contribution of degraded human performance (e.g., human errors) is significant for the safety of diverse social-technical systems. Therefore, it is crucial to understand when and why the performance of human operators could be degraded. In this study, the occurrence probability of human errors was empirically estimated based on the complexity of proceduralized tasks. To this end, Logistic regression analysis was conducted to correlate TACOM (Task Complexity) scores with human errors collected from the full-scope training simulator of nuclear power plants equipped with analog devices (analog environment). As a result, it was observed that the occurrence probability of both errors of commission and errors of omission can be soundly estimated by TACOM scores. Since the effect of diverse performance influencing factors on the occurrence probabilities of human errors could be soundly distinguished by TACOM scores, it is also expected that TACOM scores can be used as a tool to explain when and why the performance of human operators starts to be degraded.

• Journal of the Korean Society of Safety
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• v.18 no.2
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• pp.104-118
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• 2003

It has been criticized that conventional human reliability analysis (HRA) methodologies for probabilistic safety assessment (PSA) have been focused on the quantification of human error probability (HEP) without detailed analysis of human cognitive processes such as situation assessment or decision-making which are crticial to successful response to emergency situations. This paper introduces a new human reliability analysis (HRA) methodology, AGAPE-ET (A guidance And Procedure for Human Error Analysis for Emergency Tasks), focused on the qualitative error analysis of emergency tasks from the viewpoint of the performance of human cognitive function. The AGAPE-ET method is based on the simplified cognitive model and a taxonomy of influencing factors. By each cognitive function, error causes or error-likely situations have been identified considering the characteristics of the performance of each cognitive function and influencing mechanism of PIFs on the cognitive function. Then, overall human error analysis process is designed considering the cognitive demand of the required task. The application to an emergency task shows that the proposed method is useful to identify task vulnerabilities associated with the performance of emergency tasks.