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            ayesian Networks in Reliability

Langseth, Helge; Jensen, Finn Verner

doi:10.1002/9780470061572.eqr439

Cited by 57 publications

(70 citation statements)

References 21 publications

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“…In this way, BBN is a graphic representation of a multivariate probability distribution where it is possible to represent cause-effect relations among random variables (Langseth & Portinale, 2007). Moreover, BBNs provide flexibility in terms of knowledge updating through the Bayes theorem (see Bernardo & Smith (1994) for basic concepts on Bayesian inference) as discussed in following section.…”

Section: Bbn Structurementioning

confidence: 99%

“…Some recent applications are Langseth & Portinale (2007) in which a modeling framework of reliability and its properties are discussed, and Droguett & Menezes (2007) …”

Section: Bayesian Belief Networkmentioning

confidence: 99%

“…For discrete random variables, this consists of establishing conditional probabilities tables (CPTs) for each node. These CPTs can be generated from either data bases or engineering judgments, as in Langseth & Portinale (2007).…”

Section: Bbn Structurementioning

confidence: 99%

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A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems

Moura

Droguett

2008

Pesqui. Oper.

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In this work it is proposed a model for the assessment of availability measure of fault tolerant systems based on the integration of continuous time semi-Markov processes and Bayesian belief networks. This integration results in a hybrid stochastic model that is able to represent the dynamic characteristics of a system as well as to deal with cause-effect relationships among external factors such as environmental and operational conditions. The hybrid model also allows for uncertainty propagation on the system availability. It is also proposed a numerical procedure for the solution of the state probability equations of semi-Markov processes described in terms of transition rates. The numerical procedure is based on the application of Laplace transforms that are inverted by the Gauss quadrature method known as Gauss Legendre. The hybrid model and numerical procedure are illustrated by means of an example of application in the context of fault tolerant systems.Keywords: semi-Markov processes; Bayesian belief networks; Laplace transforms; availability measure; fault tolerant systems. ResumoNeste trabalho, é proposto um modelo baseado na integração entre processos semi-Markovianos e redes Bayesianas para avaliação da disponibilidade de sistemas tolerantes à falha. Esta integração resulta em um modelo estocástico híbrido o qual é capaz de representar as características dinâmicas de um sistema assim como tratar as relações de causa e efeito entre fatores externos tais como condições ambientais e operacionais. Além disso, o modelo híbrido permite avaliar a propagação de incerteza sobre a disponibilidade do sistema. É também proposto um procedimento numérico para a solução das equações de probabilidade de estado de processos semi-Markovianos descritos por taxas de transição. Tal procedimento numérico é baseado na aplicação de transformadas de Laplace que são invertidas pelo método de quadratura Gaussiana conhecido como Gauss Legendre. O modelo híbrido e procedimento numérico são ilustrados por meio de um exemplo de aplicação no contexto de sistemas tolerantes à falha.Palavras-chave: processos semi-Markovianos; redes Bayesianas; transformadas de Laplace; disponibilidade; sistemas tolerantes à falha.

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Section: Bbn Structurementioning

confidence: 99%

“…Some recent applications are Langseth & Portinale (2007) in which a modeling framework of reliability and its properties are discussed, and Droguett & Menezes (2007) …”

Section: Bayesian Belief Networkmentioning

confidence: 99%

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A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems

Moura

Droguett

2008

Pesqui. Oper.

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“…14 While conventional techniques such as fault trees fail to capture this dynamic behaviour, Bayesian belief networks (BBNs) support reasoning under uncertainty and constitute a flexible and powerful probabilistic modelling framework that makes them suitable for applications in the field of reliability and maintenance. 15,16 Recently, influence diagrams and BBNs have been used for modelling the reliability of civil structures. Models have been used to optimize the acquisition of condition data of structures 17 and optimize the maintenance of individual structures.…”

Section: Technology Reviewmentioning

confidence: 99%

Intelligent integrated maintenance for wind power generation

et al. 2015

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A novel architecture and system for the provision of Reliability Centred Maintenance (RCM) for offshore wind power generation is presented. The architecture was developed by conducting a bottom-up analysis of the data required to support RCM within this specific industry, combined with a top-down analysis of the required maintenance functionality. The architecture and system consists of three integrated modules for intelligent condition monitoring, reliability and maintenance modelling, and maintenance scheduling that provide a scalable solution for performing dynamic, efficient and cost-effective preventative maintenance management within this extremely demanding renewable energy generation sector. The system demonstrates for the first time the integration of state-of-the-art advanced mathematical techniques: Random Forests, dynamic Bayesian networks and memetic algorithms in the development of an intelligent autonomous solution. The results from the application of the intelligent integrated system illustrated the automated detection of faults within a wind farm consisting of over 100 turbines, the modelling and updating of the turbines' survivability and creation of a hierarchy of maintenance actions, and the optimizing of the maintenance schedule with a view to maximizing the availability and revenue generation of the turbines.

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“…One such framework for building statistical models for complex systems is the Bayesian network (BN) framework [1][2][3]. BNs have gained popularity over the last decade [4], partly because a number of comparisons between BNs and the classical reliability formalisms have shown that BNs have significant advantages [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Inference in hybrid Bayesian networks

Langseth

Nielsen

Rumí

et al. 2009

Reliability Engineering & System Safety

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a b s t r a c tSince the 1980s, Bayesian networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability techniques (like fault trees and reliability block diagrams). However, limitations in the BNs' calculation engine have prevented BNs from becoming equally popular for domains containing mixtures of both discrete and continuous variables (the so-called hybrid domains). In this paper we focus on these difficulties, and summarize some of the last decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability.

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B ayesian Networks in Reliability

Cited by 57 publications

References 21 publications

A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems

A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems

Intelligent integrated maintenance for wind power generation

Inference in hybrid Bayesian networks

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