Bayesian belief nets for managing expert judgement and modelling reliability

Sigurðsson, Jakob; Walls, Lesley; Quigley, John

doi:10.1002/qre.410

Cited by 121 publications

(79 citation statements)

References 29 publications

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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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Section: Technology Reviewmentioning

confidence: 99%

Intelligent integrated maintenance for wind power generation

et al. 2015

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“…There is growing literature on the use of Bayesian networks (BNs) in reliability (e.g., Portinale, Bobbio and Montani, 2005;Sigurdsson, Walls and Quigley, 2001;Lee, 2001), although there is quite a broad literature on using BNs for probabilistic modeling (e.g., Spiegelhalter, 1998;Neil, Fenton and Nielson, 2000;Laskey and Mahoney, 2000;Jensen, 2001).…”

Section: Bayesian Networkmentioning

confidence: 99%

Advances in Data Combination, Analysis and Collection for System Reliability Assessment

Wilson¹,

Graves²,

Hamada³

et al. 2006

Statist. Sci.

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Abstract. The systems that statisticians are asked to assess, such as nuclear weapons, infrastructure networks, supercomputer codes and munitions, have become increasingly complex. It is often costly to conduct full system tests. As such, we present a review of methodology that has been proposed for addressing system reliability with limited full system testing. The first approaches presented in this paper are concerned with the combination of multiple sources of information to assess the reliability of a single component. The second general set of methodology addresses the combination of multiple levels of data to determine system reliability. We then present developments for complex systems beyond traditional series/parallel representations through the use of Bayesian networks and flowgraph models. We also include methodological contributions to resource allocation considerations for system relability assessment. We illustrate each method with applications primarily encountered at Los Alamos National Laboratory.

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“…Due to these valuable properties, Bayesian networks have been increasingly applied in many fields [10][11][12][13][14]. In particular, it has been found to use in reliability only in the last seven years [15]. A framework combining the diverse sources of evidence is provided by [16][17] to assess system dependability.…”

Section: Introductionmentioning

confidence: 99%