Bayesian networks in reliability

Langseth, Helge; Portinale, Luigi

doi:10.1016/j.ress.2005.11.037

Cited by 459 publications

(112 citation statements)

References 34 publications

(2 reference statements)

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“…The graphical representation is the bridging of the gap between the (high level) conditional independence statements that must be encoded in the model and the (low level) constraints, which enforce the CPD, Langseth and Portinale (2007).…”

Section: Analysis Methods With Bayesian Network (Bn)mentioning

confidence: 99%

“…Bayesian belief nets (BBNs) (Pearl, 1988;Jensen, 1996;Jensen and Nielsen, 2007;Cowell et al, 1999) are currently the method of choice for decision modeling in complex systems. Ale et al (2009) mention the most important reasons for this are: (i) they provide a high level graphical representation of the system in terms of "influences" in which the problem owner easily recognizes his/her problem; (ii) this graphical representation also serves as a user-interface with which the user can perform recalculations and updates; (iii) they incorporate the effects of proposed decisions in a natural and transparent way.…”

Section: Analysis Methods With Bayesian Network (Bn)mentioning

confidence: 99%

“…The first three studies approach to the nuclear industry and are references to several other studies. The NUREG 1792 (Kolaczkowski et al, 2005) is cited in various applications in other industries. The last three studies bring a focused approach to the aviation industry.…”

Section: Human Factors or Performance Shaping Factor (Psf)mentioning

confidence: 99%

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Method for measuring factors that affect the performance of pilots

Bandeira¹,

Correia²,

Martins

2017

TRANSPORTES

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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.

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Section: Analysis Methods With Bayesian Network (Bn)mentioning

confidence: 99%

Section: Analysis Methods With Bayesian Network (Bn)mentioning

confidence: 99%

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Method for measuring factors that affect the performance of pilots

Bandeira¹,

Correia²,

Martins

2017

TRANSPORTES

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“…One of the first articles [12] proposes a methodology exploiting the modeling by BN in the framework of the evaluation of the reliability of an infrastructure. Today we have at our disposal a number of articles of synthesis [4], [14]- [16] which give a good view of the maturity of the applicability of the RB to the reliability in the broad sense. The Institute of the control of risks (IMdR) has supported several projects to promote the modeling by BN: The IMdR project P04-7 has helped to evaluate the relevance of the modeling approach by BN in relation to the problems of operational safety posed by industrial companies.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Modeling of a Wind Turbine System in the Preliminary Design Phase

Nabdi¹,

Zaghar²,

Herrou³

2017

JESTR

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Of our days, the question of industrial competitiveness is of particular importance for the industries in all sectors of activity. The next years will be marked by persistent pressures on the cost and the quality of the systems. Of this fact, an analysis of the safety of operation constitutes a necessary approach for the improvement of the operation of the systems by acting as soon as the primary phases of the design on the settings and the factors influencing the functioning of these systems. In effect, the modeling is a critical phase in this kind of analysis and the literature presents several tools enabling this modeling. In operational safety, there is often little information available. It is therefore generally impossible to obtain the parameters describing the Act attached. That is why; modeling tools rely primarily on the judgment of experts, surveys and databases of the manufacturers to build models structured. In this perspective, the Bayesian networks are a powerful solution of modeling for complex systems to merge the knowledge of various natures within a same model. In this logic, we will present a modeling by Bayesian networks of the two concepts of turbine namely direct and indirect in the aim of constructing a decision of choice between the two concepts in the preliminary phase of design of wind turbines while presenting the method, the calculation of the probability of state and the validation criteria of choice.

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“…[9], and the unique bidirectional inference mechanism which can be used either to predict the probability or to update the probability of known variables as well as diagnostic [8]. In recent years, BNs have become popular as a robust alternative to most classical methods such as FT [2,5], ET [10], Bow-tie(BT) [6] etc.…”

Section: Introduction Of Bn and Dbnmentioning

confidence: 99%

A Bayesian networks approach for event tree time-dependency analysis on phased-mission system

Tao

Jia

2015

Eksploatacja i Niezawodnosc - Maintenance and Reliability

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Article citation info: IntroductionAmong several techniques available to model sequence and quantify the failure probability in probabilistic risk assessment (PRA), event trees (ETs) are the most recognized methods that develop logical relationship among the events leading to the possible consequences, while fault trees (FTs) best represent the logic corresponding to pivotal events (PEs) and estimate the probabilities [16].Dependencies in event tree/ fault tree (E/FT) model are frequently encountered, and, if neglected, may result in an error estimation. Hosseini and Takahashi [4] classify dependencies into two categoriesimplicit and explicit. Explicit dependencies are due to shared basic events (SBEs) such as shared utilities or shared components which appear in more than one corresponding FTs, while the expression of implicit dependencies is a bit vague. Nývlt and Rausand [13] expanded the before-mentioned division to cover more types of dependencies such as common cause failures and cascading effect, and further classified the explicit dependencies with static and dynamic behaviour. However, in practice of aerospace PRA, such as lunar exploration which has the characteristics of the phased-mission system (PMS), ETs are typically used to portray progressions of phase mission over time, and the time interval between pivotal events (PEs) is not negligible, dependencies therefore become phase-dependency (as a subset of time-dependency in this context), and make the E/FT based reliability and risk analysis more difficult [1,13].In ET analysis, not so much work has been done with time-dependency analysis, and the papers cited above are mainly based on the hypothesis about static or time-independent behaviour [1,4,13,23]. PMS reliability attracts substantial attentions, and various techniques have been developed to deal with the phase-dependency. The analytical techniques for the PMS can be classified into two categories: combinatorial models (e.g., mini-components, sum of disjoint phase products, BDD) and state-space transition models (e.g., Markov models, Petri nets) [19,21]. The combinatorial method is based on the JiA M. A Bayesian networks approach for event tree time-dependency analysis on phased-mission system. Eksploatacja i Niezawodnosc -Maintenance and Reliability 2015; 17 (2): 273-281, http://dx.doi.org/10.17531

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Bayesian networks in reliability

Cited by 459 publications

References 34 publications

Method for measuring factors that affect the performance of pilots

Method for measuring factors that affect the performance of pilots

Bayesian Modeling of a Wind Turbine System in the Preliminary Design Phase

A Bayesian networks approach for event tree time-dependency analysis on phased-mission system

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