Open AccessProceedings Article10.1109/ICPHM.2014.7036384

Computational algorithm for dynamic hybrid Bayesian network in on-line system health management applications

- 22 Jun 2014

- pp 1-8

TL;DR: A new computational algorithm for reliability inference with dynamic hybrid Bayesian network is presented, designed to be flexible and intuitive, and scalable from small localized functionality to large complex dynamic systems.

Abstract: This paper presents a new computational algorithm for reliability inference with dynamic hybrid Bayesian network It features a component-based algorithm and structure to represent complex engineering systems characterized by discrete functional states (including degraded states), and models of underlying physics of failure, with continuous variables The methodology is designed to be flexible and intuitive, and scalable from small localized functionality to large complex dynamic systems Markov Chain Monte Carlo (MCMC) inference is optimized using pre-computation and dynamic programming for real-time monitoring of system health The scope of this research includes new modeling approach, computation algorithm, and an example application for on-line System Health Management

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This research presents a new modeling approach, computational algorithm, and an example application for health monitoring and learning in on-line SHM.. The scope of this research includes a new modeling approach, computation algorithm, and an example application for on-line SHM.

Figure 4-8: BN of components with common factor.

Citations

•Journal Article•10.1613/JAIR.1.11228

A Review of Inference Algorithms for Hybrid Bayesian Networks

Antonio Salmerón, +4 more

- 01 Aug 2018

- Journal of Artificial Intelligence Resea...

TL;DR: An overview of the main trends and principled approaches for performing inference in hybrid Bayesian networks is provided and an overview of established software systems supporting inference in these types of models is provided.

...read moreread less

•10.36001/PHMCONF.2015.V7I1.2646

Towards a methodology for design of prognostic systems

Jose Ignacio Aizpurua, +1 more

- 19 Oct 2015

TL;DR: A design decision framework is presented which guides the designer towards a prognostic algorithm through a cause-effect flowchart and demonstrates the applicability of the methodology to the design of prognostic systems which consistently meet the established requirements.

...read moreread less

•Journal Article•10.1177/1687814017728853

Investigation of Bayesian network for reliability analysis and fault diagnosis of complex systems with real case applications

Shen Chen, +4 more

- 27 Oct 2017

- Advances in Mechanical Engineering

TL;DR: In this article, the authors investigated the reliability analysis and fault diagnosis of complex engineering systems through two real cases, including a high-speed train and a critical subsystem of a high power solid-state laser.

...read moreread less

Proceedings Article•10.1109/RAMS.2015.7105184

Hybrid DBN monitoring and anomaly detection algorithms for on-line SHM

Chonlagarn Iamsumang, +2 more

- 11 May 2015

TL;DR: A hybrid Dynamic Bayesian network (DBN) is introduced with component-based structure to represent complex engineering systems with underlying physics of failure by modeling an empirical degradation model with continuous variables.

...read moreread less

Book Chapter•10.1007/978-3-319-56126-4_1

Advanced Signal Processing for Structural Health Monitoring

Ruqiang Yan, +2 more

- 01 Jan 2017

TL;DR: This chapter starts with an introduction on structural health monitoring (SHM) and emphasizes its importance for engineering systems, followed by review of each signal processing technique used in SHM.

...read moreread less

References

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Bayesian networks and decision graphs

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TL;DR: The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams, and presents a thorough introduction to state-of-the-art solution and analysis algorithms.

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Dynamic bayesian networks: representation, inference and learning

Kevin Murphy, +1 more

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TL;DR: This thesis will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in Dbns, and how to learn DBN models from sequential data.

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Journal Article•10.1016/0004-3702(86)90072-X

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The computational complexity of probabilistic inference using Bayesian belief networks (research note)

Gregory F. Cooper

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TL;DR: In this article, it was shown that probabilistic inference using belief networks is NP-hard and that it seems unlikely that an exact algorithm can be developed to perform inference efficiently over all classes of belief networks and that research should be directed toward the design of efficient special-case, average-case and approximation algorithms.

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•Journal Article•10.1016/0004-3702(93)90036-B

Approximating probabilistic inference in Bayesian belief networks is NP-hard

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Computational algorithm for dynamic hybrid Bayesian network in on-line system health management applications

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Citations

A Review of Inference Algorithms for Hybrid Bayesian Networks

Towards a methodology for design of prognostic systems

Investigation of Bayesian network for reliability analysis and fault diagnosis of complex systems with real case applications

Hybrid DBN monitoring and anomaly detection algorithms for on-line SHM

Advanced Signal Processing for Structural Health Monitoring

References

Bayesian networks and decision graphs

Dynamic bayesian networks: representation, inference and learning

Fusion, propagation, and structuring in belief networks

The computational complexity of probabilistic inference using Bayesian belief networks (research note)

Approximating probabilistic inference in Bayesian belief networks is NP-hard

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