scholarly journals Performer selection in Human Reliability analysis: D numbers approach

2019 ◽  
Vol 14 (3) ◽  
pp. 437-452 ◽  
Author(s):  
Jie Zhao ◽  
Yong Deng
Keyword(s):  
Reliability Analysis ◽  
Human Error ◽  
Multiple Criteria Decision Making ◽  
Experimental Result ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Human Errors ◽  
Factors Influencing ◽  
D Numbers ◽  
Human Error Probability

Dependence assessment among human errors in human reliability analysis (HRA) is an significant issue. Many previous works discussed the factors influencing the dependence level but failed to discuss how these factors like "similarity of performers" determine the final result. In this paper, the influence of performers on HRA is focused, in addition, a new way of D numbers which is usually used to handle with the multiple criteria decision making (MCDM) problems is introduced as well to determine the optimal performer. Experimental result demonstrates the validity of proposed methods in choosing the best performers with lowest the conditional human error probability (CHEP) under the same circumstance.

UNCERTAINTY OF HUMAN ERROR AND FUZZY APPROACH TO HUMAN RELIABILITY ANALYSIS

2006 ◽  
Vol 14 (01) ◽  
pp. 111-129 ◽  
Author(s):  
B. J. KIM ◽  
RAM R. BISHU
Keyword(s):  
Reliability Analysis ◽  
Nuclear Power ◽  
Power Plants ◽  
Human Error ◽  
Critical Factor ◽  
Fuzzy Approach ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Human Errors ◽  
Fuzzy Logic Approach

Human error is regarded as a critical factor in catastrophic accidents such as disasters at nuclear power plants, air plane crashes, or derailed trains. Several taxonomies for human errors and methodologies for human reliability analysis (HRA) have been proposed in the literature. Generally, human errors have been modeled on the basis of probabilistic concepts with or without the consideration of cognitive aspects of human behaviors. Modeling of human errors through probabilistic approaches has shown a limitation on quantification of qualitative aspects of human errors and complexity of attributes from circumstances involved. The purpose of this paper is to investigate the methodologies for human reliability analysis and introduce a fuzzy logic approach to the evaluation of human interacting system's reliability. Fuzzy approach could be used to estimate human error effects under ambiguous interacting environments and assist in the design of error free work environments.

An Application of CREAM for Human Reliability Analysis in Power System Switching Operation

2014 ◽  
Vol 584-586 ◽  
pp. 2585-2588 ◽  
Author(s):  
Jun Xi Tang ◽  
Ying Kai Bao ◽  
Li Cheng Wang ◽  
Chuang Xin Guo ◽  
Wen Hai Liu ◽  
...  
Keyword(s):  
Power System ◽  
Reliability Analysis ◽  
Human Error ◽  
Analysis Method ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Switching Operation ◽  
Equipment Reliability ◽  
System Switching ◽  
Human Error Probability

Reliability is always one of the most focal points for the power system researchers. With the improvement of equipment reliability, human error has become a great threat to the power system security. But the human reliability analysis in power system does not get as much attention as it perhaps deserve. As a representative HRA method, the Cognitive Reliability and Error Analysis Method (CREAM) is cited in this paper. And it is applied to evaluate the human error probability of a simple switching operation case. This practice is a beneficial attempt to introduce HRA method into the power system reliability research and lay a foundation for the further explorations.

Human Reliability Analysis of Ship Maneuvers in Harbor Areas

2019 ◽  
Author(s):  
Danilo T. M. P. Abreu ◽  
Marcos C. Maturana ◽  
Marcelo R. Martins ◽  
Siegberto R. Schenk
Keyword(s):  
Reliability Analysis ◽  
Human Error ◽  
Human Performance ◽  
Performance Model ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Human Errors ◽  
Ship Maneuvers ◽  
Open Sea ◽  
Expert’S Opinion

Abstract During a ship life cycle, one of the most critical phases in terms of safety refers to harbor maneuvers, which take place in restricted and congested waters, leading to higher collision and grounding risks in comparison to open sea navigation. In this scenario, a single accident may stop the harbor’s traffic as well as incur into patrimonial damage, environmental pollution, human casualties and reputation losses. In order to support the vessel’s captain during the maneuver, local experienced maritime pilots stay on board coordinating the ship navigation while in restricted waters. Because of their shorter relative duration, harbor maneuvers accidents are more probable to occur due to human errors — reinforced by the inherent surrounding difficulties —, rather than machinery failures, for instance. The human errors are object of study of the human reliability analysis (HRA). Aiming to assess the main factors contributing to human errors in pilot-assisted harbor ship maneuvers, this work proposes a Bayesian network model for HRA, supported by a prospective human performance model for quantification. Similar works focus mainly on open sea navigation and collision accidents, which do not reflect the strict conditions found on port areas. Additionally, most of the models are highly dependent on expert’s opinion for quantification. Therefore, the novelty of this work resides into two aspects: a) incorporation of harbor specific conditions for maritime navigation HRA, including the performance of ship’s crew and maritime pilots; and b) the use of a prospective human performance model as an alternative to expert’s opinion for quantification purposes. To illustrate the usage of the proposed methodology, this paper presents an analysis of the route keeping task along waterways, starting from the quantification of human error probabilities (HEP) and including the ranking of the main external factors that contribute to the HEP.

Human Reliability, Experience and Error Probability: A New Benchmark

2009 ◽  
Author(s):  
Romney B. Duffey ◽  
Tae Sung Ha
Keyword(s):  
Reliability Analysis ◽  
Error Probability ◽  
Human Error ◽  
Accident Risk ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
New Approach ◽  
Learning Hypothesis ◽  
Independent Assessment ◽  
Human Error Probability

In this paper, we apply a totally new approach to benchmark human reliability analysis, which is derived directly from the Universal Learning Curve (ULC) for homo-technological system (HTS) outcomes. We compare the latest second-generation predictions, based on the well-known and proven learning hypothesis, against some of the common Human Reliability Analysis (HRA) methods used to date for analyzing human reliability in transient and accident analysis. Therefore, we provide a straightforward, general and simple methodology for evaluating and predicting the Human Error Probability (HEP) in transients for accident risk prediction and reduction, as validated against all the available data, producing a completely independent assessment of the uncertainty.

scholarly journals Human reliability analysis in de-energization of power line using HEART in the context of Z-numbers

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253827
Author(s):  
Hamed Aghaei ◽  
Mostafa Mirzaei Aliabadi ◽  
Farzaneh Mollabahrami ◽  
Kamran Najafi
Keyword(s):  
Reliability Analysis ◽  
Human Error ◽  
Critical Issue ◽  
Power Line ◽  
Electricity Distribution ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Straightforward Method ◽  
Human Error Probability

Investigation reveals that a high percentage of incident causes are ascribed to some forms of human error. To effectively prevent incidents from happening, Human Reliability Analysis (HRA), as a structured way to represent unintentional operator contribution to system reliability, is a critical issue. Human Error Reduction and Assessment Technique (HEART) as a famous HRA technique, provides a straightforward method to estimate probabilities of human error based on the analysis of tasks. However, it faces varying levels of uncertainty in assigning of weights to each error producing condition (EPC), denoted as assessed proportion of affect (APOA), by experts. To overcome this limitation and consider the confidence level (reliability or credibility) of the experts, the current study aimed at proposing a composite HEART methodology for human error probability (HEP) assessment, which integrates HEART and Z-numbers short for, Z-HEART. The applicability and effectiveness of the Z-HEART has been illustrated in the de-energization power line as a case study. Furthermore, a sensitivity analysis is fulfilled to investigate the validity of the proposed methodology. It can be concluded that Z-HEART is feasible for assessing human error, and despite the methodological contributions, it offers many advantages for electricity distribution companies.

scholarly journals Human reliability analysis of high-temperature molten metal operation based on fuzzy CREAM and Bayesian network

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254861
Author(s):  
Yaju Wu ◽  
Kaili Xu ◽  
Ruojun Wang ◽  
Xiaohu Xu
Keyword(s):  
High Temperature ◽  
Bayesian Network ◽  
Molten Metal ◽  
Error Probability ◽  
Human Error ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Human Errors ◽  
Metallurgy Industry ◽  
Human Error Probability

Human errors are considered to be the main causation factors of high-temperature molten metal accidents in metallurgical enterprises. The complex working environment of high- temperature molten metal in metallurgical enterprises has an important influence on the reliability of human behavior. A review of current human reliability techniques confirms that there is a lack of quantitative analysis of human errors in high-temperature molten metal operating environments. In this paper, a model was proposed to support the human reliability analysis of high-temperature molten metal operation in the metallurgy industry based on cognitive reliability and error analysis method (CREAM), fuzzy logic theory, and Bayesian network (BN). The comprehensive rules of common performance conditions in conventional CREAM approach were provided to evaluate various conditions for high-temperature molten metal operation in the metallurgy industry. This study adopted fuzzy CREAM to consider the uncertainties and used the BN to determine the control mode and calculate human error probability (HEP). The HEP for workers involved in high-temperature melting in steelmaking production process was calculated in a case with 13 operators being engaged in different high-temperature molten metal operations. The human error probability of two operators with different control modes was compared with the calculation result of basic CREAM, and the result showed that the method proposed in this paper is validated. This paper quantified point values of human error probability in high-temperature molten metal operation for the first time, which can be used as input in the risk evaluation of metallurgical industry.

A Method for Marine Human Error Probability Estimate: APJE-SLIM

2011 ◽  
Vol 97-98 ◽  
pp. 825-830 ◽  
Author(s):  
Yong Tao Xi ◽  
Chong Guo
Keyword(s):  
Collision Avoidance ◽  
Human Error ◽  
Shipping Industry ◽  
Human Reliability ◽  
Combined Method ◽  
Probability Estimate ◽  
Human Errors ◽  
Performance Shaping Factors ◽  
Industry Research ◽  
Human Error Probability

Safety is the eternal theme in shipping industry. Research shows that human error is the main reason of maritime accidents. Therefore, it is very necessary to research marine human errors, to discuss the contexts which caused human errors and how the contexts effect human behavior. Based on the detailed investigation of human errors in collision avoidance behavior which is the most key mission in navigation and the Performance Shaping Factors (PSFs), human reliability of mariners in collision avoidance was analyzed by using the integration of APJE and SLIM. Result shows that this combined method is effective and can be used for the research of maritime human reliability.

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