Human reliability analysis of the Tokai-Mura accident through a THERP–CREAM and expert opinion auditing approach

Ribeiro, Ana Cecília; Sousa, Abraham L. R. de; Duarte, Juliana P.; Melo, Prı́amo A.

doi:10.1016/j.ssci.2016.04.009

Cited by 28 publications

(15 citation statements)

References 5 publications

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“…The physiological aspects of human error refer to the limits of human ability, including human perception, feelings, reaction speed, physical strength, and biological rhythm, and human psychological aspects refer to temperament, character, emotions, and attention. Ribeiro and Sousa et al (2016) proposed a human reliability analysis model that combines the features related to facility conditions to determine the probability of human error in the probabilistic safety analysis of a process plant and to prove that the human factor is the leading cause of accidents [31]. Erga et al (2016) studied the framework for the classification and analysis of human factors related to accidents and events [32].…”

Section: Human Reliability Researchmentioning

confidence: 99%

Exploring the Optimal Safety Person–job Matching Method of Major Equipment Based on Human Reliability

Zhang

et al. 2019

Applied Sciences

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Under the background of intelligent manufacturing, this paper aims to develop a model for person–job safe matching that optimizes safety with consideration of major equipment operator competency and task complexity. Safe matching cost is minimized in the developed model and is measured by the equipment downtime, production defect rate, and operator labor costs oriented by human factors. Human reliability is calculated with the goal of best value individual competency and best admit task complexity with a hierarchical structure. The 0-1 integer programming person–job matching model minimizes the human factor safety and wage costs and satisfies the requirements of the production order, budget and operator quantity requirement. An improved genetic algorithm is designed to solve the model. The computational results of the proposed model based on a case study for a large iron and steel company evidently demonstrated its effectiveness. A new integrated model provides more realistic matches for person–job assignment.

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Section: Human Reliability Researchmentioning

confidence: 99%

Exploring the Optimal Safety Person–job Matching Method of Major Equipment Based on Human Reliability

Zhang

et al. 2019

Applied Sciences

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“…However, the two inherently deterministic methods arguably lack capability of dealing with the uncertainties in common performance condition (CPC) configuration (Kim et al, 2006) and different weight assignments (He et al, 2008) to the CPCs in traditional CREAM. Recently, some studies have been conducted for the improvement of CREAM and HEP estimation by means of probabilistic techniques (Kim et al, 2006;Fujita and Hollnagel, 2004), fuzzy approaches (Konstandinidou et Akyuz (2015) constructed a risk-based CREAM model for HEP quantification towards the gas inerting process on-board crude oil tankers and Ribeiro et al (2016) presented a hybrid THERP-CREAM method to analysis the human reliability of Tokai-Mura accidents. Although showing attractiveness in terms of providing solutions to some of the inherent drawbacks of CREAM, such methods have not yet well addressed the incompleteness in subjective data from experts, revealing the need for further studies in the field.…”

Section: Creammentioning

confidence: 99%

A new hybrid approach to human error probability quantification–applications in maritime operations

Yang

Fang

et al. 2017

Ocean Engineering

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A new hybrid approach to human error probability quantification-applications in maritime operations http://researchonline.ljmu.ac.uk/id/eprint/6876/ Article LJMU has developed LJMU Research Online for users to access the research output of the University more effectively.Xi, YT, Yang, ZL, Fang, QG, Chen, WJ and Wang, J (2017) A new hybrid approach to human error probability quantification-applications in maritime operations. Ocean Engineering, 138. pp. 45-54. AbstractHuman Reliability Analysis (HRA) has always been an essential research issue in safety critical systems. Cognitive Reliability Error Analysis Method (CREAM), as a well-known second generation HRA method is capable of conducting both retrospective and prospective analysis, thus being widely used in many sectors. However, the needs of addressing the use of a deterministic approach to configure common performance conditions (CPCs) and the assignment of the same importance to all the CPCs in a traditional CREAM method reveal a significant research gap to be fulfilled. This paper describes a modified CREAM methodology based on an Evidential Reasoning (ER) approach and a Decision Making Trial and Evaluation Laboratory (DEMATEL) technique for making human error probability quantification in CREAM rational. An illustrative case study associated with maritime operation is presented. The proposed method is validated by sensitivity analysis and the quantitative analysis result is verified through comparing the data and the benchmarking with the real data collected from Shanghai coastal waters. Its main contribution lies in that it for the first time addresses the data incompleteness in HEP, given that the previous relevant studies mainly focus on the fuzziness in data. The findings will provide useful insights for quantitative assessment of seafarers' errors to reduce maritime risks due to human errors.

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“…At the moment, there are more than a dozen HRA methods for HEP in different systems, such as THERP (10), ASEP (11,12), ATHEANA (13), SHERPA (14), CREAM (15), SPAR-H (16), and new methods are being developed. Today, various HRA methods have been utilized in multiple engineering applications (17)(18)(19)(20)(21)(22). For example, Mandal et al (18) identified probability of human error through overhead crane operation tasks using SHERPA methodology.…”

Section: Introductionmentioning

confidence: 99%

Human Reliability Analysis (HRA) Using Standardized Plant Analysis Risk-Human (SPAR-H) and Bayesian Network (BN) for Pipeline Inspection Gauges (PIG) Operation: A Case Study in a Gas Transmission Plant

et al. 2019

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Background: Pigging operation is one of the maintenance activities that is used to check pipeline functionality in operational conditions using a PIG device and high pressure of liquid/gas, which is potentially hazardous. Objectives: The present study aimed at customizing SPAR-H methodology for the pigging operation using Bayesian networks (BNs). It also aimed at identifying and analyzing human errors in pigging operation in a gas transmission company. Methods: The current article was composed of two main steps. In the first step, the SPAR-H BN model was developed using expertelicited prior probabilities of pigging operation applied to Bayesian network. In this step, CPTs of PSF nodes are constructed using prior probabilities, which are achieved from expert opinion. The CPT of error node is developed using coding process of SPAR-H formula in a simulation node. In the second step, a descriptive study was carried out to estimate the probability of human errors in pigging operation in a gas transmission plant in Iran. First, hierarchical task analysis (HTA) was conducted by walking through the pigging operation and interviews with workers. Next, the SPAR-H BN model was utilized for estimation of human error probability. Results: The developed model was tested on the pigging operation subtasks. In the considered case study, the mean probability of human error was estimated as 0.184. The highest probability of human error was related to "opening the kicker valve for enhancing pressure" subtask. Conclusions: The BNs were helpful to adapt the SPAR-H methodology to the pigging operation using dedicated prior probabilities. Beside that, the probabilities of human error can be updated taking into account the more realistic operational and environmental conditions.

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Human reliability analysis of the Tokai-Mura accident through a THERP–CREAM and expert opinion auditing approach

Cited by 28 publications

References 5 publications

Exploring the Optimal Safety Person–job Matching Method of Major Equipment Based on Human Reliability

Exploring the Optimal Safety Person–job Matching Method of Major Equipment Based on Human Reliability

A new hybrid approach to human error probability quantification–applications in maritime operations

Human Reliability Analysis (HRA) Using Standardized Plant Analysis Risk-Human (SPAR-H) and Bayesian Network (BN) for Pipeline Inspection Gauges (PIG) Operation: A Case Study in a Gas Transmission Plant

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