A dynamic discretization method for reliability inference in Dynamic Bayesian Networks

Zhu, Jiandao; Collette, Matthew

doi:10.1016/j.ress.2015.01.017

Cited by 53 publications

(24 citation statements)

References 16 publications

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“…Since the conditional probability tables are dependent upon where state bin thresholds are located, adjusting the bin boundaries will have a significant impact on the conditional probability tables that the model is dependent on. Previous work by Zhu and Collette [29] has found attempts to discretize the bins dynamically. This study would also be associated with identifying the appropriate number of bins, based on the findings from Yang and Webb [30].…”

Section: Discussionmentioning

confidence: 99%

A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems

Lewis

Groth

2020

Algorithms

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Dynamic Bayesian networks (DBNs) represent complex time-dependent causal relationships through the use of conditional probabilities and directed acyclic graph models. DBNs enable the forward and backward inference of system states, diagnosing current system health, and forecasting future system prognosis within the same modeling framework. As a result, there has been growing interest in using DBNs for reliability engineering problems and applications in risk assessment. However, there are open questions about how they can be used to support diagnostics and prognostic health monitoring of a complex engineering system (CES), e.g., power plants, processing facilities and maritime vessels. These systems’ tightly integrated human, hardware, and software components and dynamic operational environments have previously been difficult to model. As part of the growing literature advancing the understanding of how DBNs can be used to improve the risk assessments and health monitoring of CESs, this paper shows the prognostic and diagnostic inference capabilities that are possible to encapsulate within a single DBN model. Using simulated accident sequence data from a model sodium fast nuclear reactor as a case study, a DBN is designed, quantified, and verified based on evidence associated with a transient overpower. The results indicate that a joint prognostic and diagnostic model that is responsive to new system evidence can be generated from operating data to represent CES health. Such a model can therefore serve as another training tool for CES operators to better prepare for accident scenarios.

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Section: Discussionmentioning

confidence: 99%

A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems

Lewis

Groth

2020

Algorithms

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“…Regarding the last type of method, Mahadevan et al [58] validated the proposed BN-based structural system reliability evaluation methodology by comparing its results with those of traditional reliability methods. Zhu and Collette [63] proposed a dynamic discretization method for DBN inference for structural reliability analysis; the robustness and efficiency of the method were validated by comparing it with the existing ones by using crack growth examples. Straub [57] validated a DBN-based stochastic model for structural reliability analysis by comparing its results with those of a second-order reliability method and Monte Carlo simulation.…”

Section: B Structural Reliability Evaluation With Bnsmentioning

confidence: 99%

Application of Bayesian Networks in Reliability Evaluation

Cai

Kong

et al. 2019

IEEE Trans. Ind. Inf.

194

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The Bayesian network (BN) is a powerful model for probabilistic knowledge representation and inference and is increasingly used in the field of reliability evaluation. This paper presents a bibliographic review of BNs that have been proposed for reliability evaluation in the last decades. Studies are classified from the perspective of the objects of reliability evaluation, i.e., hardware, structures, software, and humans. For each classification, the construction and validation of a BN-based reliability model are emphasized. The general procedural steps for BN-based reliability evaluation, including BN structure modeling, BN parameter modeling, BN inference, and model verification and validation, are investigated. Current gaps and challenges in reliability evaluation with BNs are explored, and a few upcoming research directions that are of interest to reliability researchers are identified.

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“…However, an obvious shortcoming is that the impact of the component characteristic on the failure rate of network is not considered. Some examples, including the reliability of Boolean polynomial [7], minimum spanning tree [14,15], minimum cut set and minimum path set [16], and fault-tree analysis [9], attempting to incorporate 2 Complexity more features of network topology consisting of multiple terminals and dependency between topology are researched. Meanwhile, simulation based on Monte Carlo method [10] often depends more on the convergence of probability than the number of network components; statistical error during reliability analysis may result in slow convergence for achieving acceptable accuracy in low probability estimations.…”

Section: Introductionmentioning

confidence: 99%

Measuring Component Importance for Network System Using Cellular Automata

Cao

Shang

2019

Complexity

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This paper concentrates on the component importance measure of a network whose arc failure rates are not deterministic and imprecise ones. Conventionally, a computing method of component importance and a measure method of reliability stability are proposed. Three metrics are analyzed first: Birnbaum measurement, component importance, and component risk growth factor. Based on them, the latter can measure the impact of the component importance on the reliability stability of a system. Examples in some typical structures illustrate how to calculate component importance and reliability stability, including uncertain random series, parallel, parallel-series, series-parallel, and bridge systems. The comprehensive numerical experiments demonstrate that both of these methods can efficiently and accurately evaluate the impact of an arc failure on the reliability of a network system.

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A dynamic discretization method for reliability inference in Dynamic Bayesian Networks

Cited by 53 publications

References 16 publications

A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems

A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems

Application of Bayesian Networks in Reliability Evaluation

Measuring Component Importance for Network System Using Cellular Automata

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