Application of Bayesian Belief networks to the human reliability analysis of an oil tanker operation focusing on collision accidents

Martins, Marcelo Ramos; Maturana, Marcos Coelho

doi:10.1016/j.ress.2012.09.008

Cited by 153 publications

(69 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This paper proposes a generic model to analyze aircraft accidents considering the factors that contribute to the pilot's performance based on studies of Greenberg (2005;2007), Roelen et al (2011) and Martins and Maturana (2013).…”

Section: Development Of General Model Of Accidents Analysismentioning

confidence: 99%

“…In summary the model shows the conjunction of the technical fault tree and event tree to the method of Bayesian networks. Martins and Maturana (2013) analyzed the human error contribution for ship collision taking into consideration the activities performed by the crew and is focused on the operation of an oil tanker in the Brazilian coast. The authors searched evidence related to the occurrences of the possible human errors (not found in literature) according to the routine of the ship thus obtaining the necessary data to build the network.…”

Section: Development Of General Model Of Accidents Analysismentioning

confidence: 99%

“…Within a systemic view, it can be mentioned the studies of Greenberg (2007), Roelen et al (2011) and Martins and Maturana (2013). Such studies have emphasized a probabilistic approach analyzing human factors, organizational factors and other factors associated with the environment and/or local infrastructure.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Method for measuring factors that affect the performance of pilots

Bandeira¹,

Correia²,

Martins

2017

TRANSPORTES

Self Cite

View full text Add to dashboard Cite

This paper presents the development of a model of accident analysis according to the principal factors which influence aeronautical accidents that are able to assess any aircraft accident, taking into account human, organizational, environmental and airport infrastructure factors. The methodology of data collection of this research was through the literature, analysis of aircraft accident reports, technical visits to the center of certification of commercial aircraft pilots and interviews with industry experts. From this model, it is possible to evaluate the influence of these factors and identify the dependence and relationship existing, and how they influence the system. With the aid of Bayesian Networks technique, it is also possible to quantify the factors and assess which ones have more impact in the system. The results show the relationship between the factors that can influence the performance of the pilots and therefore can indicate how it may impact the success or failure of tasks related to flight procedures. The results also may indicate subsidies for mitigating actions, collaborating in the management of operational safety of air transport and assessing the overall impact of the factors that determine any accident. RESUMOEste artigo apresenta o desenvolvimento de um modelo de análise de acidentes de acordo com os principais fatores que influenciam os acidentes aeronáuticos, capaz de avaliar qualquer acidente de aeronave, levando em conta fatores de infraestrutura humana, organizacional, ambiental e aeroportuária. A metodologia de coleta de dados desta pesquisa foi através de literatura, análise de acidentes aeronáuticos, visita técnica no centro de certificação de pilotos de aeronaves comerciais e consultas a especialistas. A partir desse modelo, é possível avaliar a influência desses fatores e identificar as dependências e relações existentes, e como elas influenciam o sistema. Com a ajuda da técnica de Redes Bayesianas também é possível quantificar os fatores e avaliar quais fatores têm mais impacto no sistema. Os resultados mostram a relação entre os fatores que podem influenciar o desempenho dos pilotos e, portanto, indicam como isso pode afetar o sucesso ou o fracasso das tarefas relacionadas aos procedimentos de voo. Os resultados também podem indicar subsídios para ações mitigadoras, colaborar na gestão da segurança operacional do transporte aéreo e avaliar o impacto global dos fatores que determinam qualquer acidente.

show abstract

Section: Development Of General Model Of Accidents Analysismentioning

confidence: 99%

Section: Development Of General Model Of Accidents Analysismentioning

confidence: 99%

See 1 more Smart Citation

Method for measuring factors that affect the performance of pilots

Bandeira¹,

Correia²,

Martins

2017

TRANSPORTES

Self Cite

View full text Add to dashboard Cite

show abstract

“…Artificial intelligence learning is the assimilation of the experience in the presence of uncertainty or variability, that is, the estimation of qualitative or quantitative parameters that can generate predisposition to updates, which may be applied by an expert, or in the case of very complex problems with automatic learning methods from the available data on the problem (Liao, 2012;Martins & Maturana, 2013).…”

Section: Model Construction (Mc)mentioning

confidence: 99%

Modeling Bayesian Networks from a conceptual framework for occupational risk analysis

Vieira

Silva

2017

Prod.

View full text Add to dashboard Cite

“…In recent years, as an advanced technology for reasoning and describing uncertain knowledge, Bayesian network (BN) has been widely applied to the field of reliability analysis, risk analysis and maintenance of complex systems [9][10][11][12][13][14]. Bayesian network model can express multi-state of the event, and uncertainty of logical causality and information.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Networks Application in Multi-State System Reliability Analysis

Zhai¹,

Lin²

2013

Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation

View full text Add to dashboard Cite

Abstract-Aiming at the limitations of traditional reliability analysis theory in multi-state system, a method for reliability modeling and assessment of a multi-state system based on Bayesian Network (BN) is proposed with the advantages of uncertain reasoning and describing multi-state of event. Through the case of cell production line system, in this paper we will discuss how to establish and construct a multi-state system model based on Bayesian network, and how to apply the prior probability and posterior probability to do the bidirectional inference analysis, and directly calculate the reliability indices of the system by means of prior probability and Conditional Probability Table (CPT) . Thereby we can do the qualitative and quantitative analysis of the multi-state system reliability, identify the weak links of the system, and achieve assessment of system reliability.

show abstract

Application of Bayesian Belief networks to the human reliability analysis of an oil tanker operation focusing on collision accidents

Cited by 153 publications

References 14 publications

Method for measuring factors that affect the performance of pilots

Method for measuring factors that affect the performance of pilots

Modeling Bayesian Networks from a conceptual framework for occupational risk analysis

Bayesian Networks Application in Multi-State System Reliability Analysis

Contact Info

Product

Resources

About