An Experimental Study on Developing a Cognitive Model for Human Reliability Analysis

Falcone, Domenico; Felice, Fabio De; Petrillo, Antonella; Silvestri, Alessandro

doi:10.5772/intechopen.69230

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…In 2017, Falcone et al introduced a hybrid model that integrates the advantages of the methodologies HEART, Standardized Plant Analysis Risk-Human (SPAR-H) and Success Likelihood Index Method (SLIM) in order to evaluate the impact of all environmental and behavioural factors on the decisions and the actions of operators in case of accidents and disasters. Results obtained from the analysis of a real case study provide an empirical and a theoretical contribution referring to the framework used to detect human error, evaluating the workers' reliability under emergency conditions [23]. A model for assessing the probability of human errors in reconfigurable manufacturing systems based on tasks characteristics, work environment as well as workers capabilities has been developed using the multi-attribute utility analysis by Elmaraghy et al (2008) independently of time.…”

Section: Introduction Issn 2683-345xmentioning

confidence: 99%

Model of Human Error Probability based on dual-phase approach for learning process in cognitive-oriented tasks

Facchini¹,

Digiesi²,

Mummolo³

2020

Int J Ind Eng Manag

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New accidents or incidents due to human error do not "just happen" and in most of the cases the cause relies not only on human incompetence or negligence [1]. In scientific literature the Human Error Probability (HEP) is defined, in case of discrete tasks, as the number of errors divided by the number of opportunities for making errors. The HEP is a key element in system accidents and disasters, especially in high-risk fields, such as industrial plants, nuclear plants, and aerospace. According to Health and Safety Executive agency (HSE), the errors can be classified in two categories. The first category includes the so-called "slips or lapses" errors: they are considered "actions that were not as planned" or unintended actions. These kinds of errors occur during the completion of a familiar task and include slips (e.g. pressing the wrong button or reading the wrong gauge) and lapses (e.g. forgetting to carry out a step in a known procedure). Therefore, they cannot be avoided by means of training, but an improved workplace design can reduce their likelihood and provide a more error tolerant system. The second category of errors includes the errors of judgement or decision-making, so-called "mistakes". They occur when the worker accomplishes the wrong action believing it to be right. In most cases, a mistake occurs in situations where Human Error Probability (HEP) is a key element in the chain of events that could lead up to system accidents and despite the efforts of recently studies to evaluate the human reliability, many of the limitations and problems have not yet been solved. The model proposed tries to overcome these limitations by means of a method that combines the advantages of a dual-phase learning approach with that of a multi-attribute utility analysis, in accordance with Dar-El' s theory. Results of numerical simulations show the effectiveness of the model in quantifying, over time, the HEP and in evaluating the human task error proneness by varying the work breaks schedule.

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Section: Introduction Issn 2683-345xmentioning

confidence: 99%

Model of Human Error Probability based on dual-phase approach for learning process in cognitive-oriented tasks

Facchini¹,

Digiesi²,

Mummolo³

2020

Int J Ind Eng Manag

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Quality Checks Logit Human Reliability (LHR): A New Model to Evaluate Human Error Probability (HEP)

Bona

Falcone

Forcina

et al. 2021

Mathematical Problems in Engineering

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In the years, several approaches for human reliability analysis (HRA) have been developed. The aim of the present research is to propose a hybrid model to evaluate Human Error Probability (HEP). The new approach is based on logit-normal distribution, Nuclear Action Reliability Assessment (NARA), and Performance Shaping Factors (PSFs) relationship. In the research, shortcomings related to literature approaches are analyzed, especially the limitations of the working time. For this reason, PSFs after 8 hours (work standard) during emergency conditions were estimated. Therefore, the correlation between the advantages of these three methodologies allows proposing a HEP analysis during accident scenarios and emergencies; a fundamental issue to ensure the safety and reliability in industrial plants is emergency Mmnagement (EM). Applying EM methodology, two main aspects are analyzed: system reliability and human reliability. System reliability is strongly related to the reliability of its weakest component. During incidental situations, the weakest parts of the whole system are workers (human reliability) and accidental scenarios influence the operator’s ability to make decisions. This article proposes a new approach called Logit Human Reliability (LHR) that considers internal and external factors to estimate human reliability during emergencies. LHR has been applied in a pharmaceutical accident scenario, considering 24 hours of working time (more than 8 working hours). The results highlighted that the LHR method gives output data more in conformity with data banks than the conventional methods during the stress phase in an accident scenario.

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An Experimental Study on Developing a Cognitive Model for Human Reliability Analysis

Cited by 2 publications

References 11 publications

Model of Human Error Probability based on dual-phase approach for learning process in cognitive-oriented tasks

Model of Human Error Probability based on dual-phase approach for learning process in cognitive-oriented tasks

Quality Checks Logit Human Reliability (LHR): A New Model to Evaluate Human Error Probability (HEP)

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