• Safety and Health at Work
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• 제9권1호
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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.

• 한국안전학회지
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• 제21권3호
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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.

• 한국안전학회지
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• 제22권1호
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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.

• 한국가스학회지
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• 제6권4호
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• pp.1-7
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• 2002

인적 오류(human error)는 공장설비의 설계, 제작, 건설, 운전, 유지$\cdot$보수의 모든 단계에서 발생할 수 있으며 사고의 대부분이 인적 오류의 영향과 관계되어 있는 것으로 조사되고 있다. 본 연구에서는 현장의 작업자 행동 특성 및 오류 메커니즘을 확인하고, 평가분류 쉬트를 활용하여 공정에서 발생하는 인적 오류를 분석하였다. 또한 ASEP HRA 절차를 이용하여 인적 오류 확률(HEP) 산정 알고리즘을 구축하여 현장에서 쉽게 인적 오류를 분석할 수 있는 ASEP HEP 프로그램을 개발하였다. 이를 이용하여 화학공장에서의 가능한 인적 오류사고를 예방하고 보다 체계적인 인적 오류 방지대책을 수립할 수 있다고 판단된다.

• Safety and Health at Work
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• 제7권1호
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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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• 제23권2호
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• pp.174-182
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• 1991

안전계통의 이용불능도 및 최적시험주기 평가에 있어서 주기적인 시험과 관련된 인간실수의 영향을 고려하였다. 시험 및 보수와 관련된 인간실수는 건전한 계통이 시험 후 잘못된 상태에 놓이게 될 가능성과(Type A인간실수)건전하지 못한 계통이 시험시 감지되지 못할 가능성(Type B인간실수)이다. 계통이용불능도 및 최적시험주기에 미치는 인간실수의 영향을 결정하기 위하여 안전계통의 이용불능도를 계산하기 위한 사상수목모델이 개발되었다. 또한 안전주입계통의 신뢰도 분석을 통하여 계통전체에 미치는 영향을 평가하였다. 다양한 민감도 분석 결과, (1) 계통이용불능도는 인간실수의 확률이 커질수록 증가하며 Type A인간실수의 영향이 훨씬 크다. (2) 최적시험주기 는 Type A 인간실수가 커 질수록 약간 증가하나, Type B 인간실수가 커 질수록 감소한다. (3) 안전주입펌프의 시험주기를 고정시키면 안전주입계통의 이용불능도는 Type A인간실수가 커질수록 크게 증가하나 Type B 인간실수가 커지더라도 약간 증가한다. 따라서 인간실수의 영향을 고려 할 때 계통의 이용불능도를 일정 수준으로 유지하기 위해서는 시험주기(최적시험주기가 아님 )를 줄여야 한다. 그리고 시험 및 보수시 Type A 인간실수는 계통의 이용불능도에 미치는 영향이 크므로, 특히 TyPe A 인간실수를 줄이기 위한 노력이 필요하다.

• 한국항공운항학회지
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• 제29권2호
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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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• 제45권2호
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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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• 제54권6호
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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.

• 한국안전학회지
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• 제18권2호
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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.