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Bayesian Networks and Evidence Theory to Model Complex Systems Reliability

Christophe Simon, Philippe Weber, Eric Levrat
2007 Journal of Computers  
We propose to adapt the Bayesian Network model of reliability in order to integrate the evidence theory and then to produce an Evidential Network.  ...  This paper deals with the use of Bayesian Networks to compute system reliability of complex systems under epistemic uncertainty.  ...  To model reliability in the epistemic context of uncertainty, the combination of the evidence theory and the Bayesian Networks offers a very interesting tool.  ... 
doi:10.4304/jcp.2.1.33-43 fatcat:qg2n7imvkbgh5o3pneuywdwxcm

Reliability analysis of an engine under uncertainty based on D-S evidence theory and Bayesian network

Ting Xue Xu, Zhi Qiang Li, Jun Yuan Gu, Lin Yu Fu, Qi Dong
2017 Mathematical Models in Engineering  
There are many methods applied including Bayesian network and D-S evidence theory to cope with uncertainty involving aleatory uncertainty and epistemic uncertainty in reliability analysis of complex systems  ...  Bayesian network model is established by referring to the fault tree of the engine, and D-S evidence theory is used to determine the belief functions and plausibility functions of uncertain nodes by data  ...  The development of Bayesian network, Markov model, Petri net, and Fractional Calculus theory promotes the research of reliability analysis in multi-states of complex systems and units.  ... 
doi:10.21595/mme.2017.19015 fatcat:sfoetwpfrzcobm3yn6lh4n2pne

Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis

C. Simon, P. Weber, A. Evsukoff
2008 Reliability Engineering & System Safety  
Finally, it is shown, with a numerical example, how Bayesian networks' inference algorithms compute complex system reliability and what the Dempster Shafer theory can provide to reliability analysis.  ...  This paper deals with the use of Bayesian networks to compute system reliability.  ...  In this article, we propose to combine the evidence theory with Bayesian networks to model system reliability.  ... 
doi:10.1016/j.ress.2007.03.012 fatcat:dvklyp7quzch7jtkpc77d5atf4

Bayesian networks implementation of the Dempster Shafer theory to model reliability uncertainty

C. Simon, P. Weber
2006 First International Conference on Availability, Reliability and Security (ARES'06)  
In this paper, the implementation of the Dempster Shafer theory in a Bayesian Network tool is proposed in order to compute system reliability and manage epistemic uncertainty propagation.  ...  Probabilistic tools badly handle these kinds of problems thus, it is better to use formalism from the evidence theory.  ...  Moreover, we take advantage of the power of the Bayesian Network tools to model system reliability. Thus, the second section of the paper is dedicated to the basics of the evidence theory.  ... 
doi:10.1109/ares.2006.38 dblp:conf/IEEEares/SimonW06 fatcat:lhos5swjurb5zhdne23gncbl3e

Bayesian Networks Application in Multi-State System Reliability Analysis

Sheng Zhai, Shu Zhong Lin
2013 Applied Mechanics and Materials  
Aiming at the limitations of traditional reliability analysis theory in multi-state system, a method for reliability modeling and assessment of a multi-state system based on Bayesian Network (BN) is proposed  ...  Through the case of cell production line system, in this paper we will discuss how to establish and construct a multi-state system model based on Bayesian network, and how to apply the prior probability  ...  ACKNOWLEDGMENT We would like to thank Tianjin Weituo Automation Technology Co., Ltd. for support.  ... 
doi:10.4028/ fatcat:4545iy7uj5cjlb4bho4ocdte2e

Reliability Analysis of a Complex Multistate System Based on a Cloud Bayesian Network

Jin-Zhang Jia, Zhuang Li, Peng Jia, Zhi-Guo Yang, Baoping Cai
2021 Shock and Vibration  
This study combined a cloud model, Bayesian network, and common-cause failure theory to expand a Bayesian network by incorporating cloud model theory.  ...  The cloud model and Bayesian network were combined to form a reliable cloud Bayesian network analysis method.  ...  thank LetPub ( for its linguistic assistance during the preparation of this manuscript. is research was supported by the National Natural Science Foundation of China (51374121) and  ... 
doi:10.1155/2021/6660928 fatcat:yrsfgcbhtjgv3oj7tljdaqj5ai

Bayesian Networks for Reliability Analysis of Complex Systems [chapter]

José Gerardo Torres-Toledano, Luis Enrique Sucar
1998 Lecture Notes in Computer Science  
This paper presents an extension of Bayesian networks (BN) applied to reliability analysis. We developed a general methodology for modelling reliability of complex systems based on Bayesian networks.  ...  We developed a general methodology for modelling reliability of complex systems based on Bayesian networks.  ...  The second part summarizes the theory of Bayesian networks and general aspects of reliability analysis.  ... 
doi:10.1007/3-540-49795-1_17 fatcat:467wnoyc5fgxfoepkdhdmrcise

Reliability modelling with dynamic bayesian networks

Weber P., Jouffe L.
2003 IFAC Proceedings Volumes  
The work reported here presents a methodology for developing Dynamic Bayesian Networks (DBN) to formalise such complex dynamic models.  ...  A small valve system then is used to compare the reliability estimations obtained by the proposed DBN model and by the classical Markov Chain.  ...  This method is based on Dynamic Bayesian Networks. BAYESIAN NETWORK THEORY BNs are probabilistic networks based on graph theory.  ... 
doi:10.1016/s1474-6670(17)36470-4 fatcat:nxidxhtn6jcrfjnn6nf7yser2u

Assessing dependability of safety critical systems using diverse evidence

N. Fenton, B. Littlewood, M. Neil, L. Strigini, A. Sutcliffe, D. Wright
1998 IEE Proceedings - Software  
To implement this approach for combining evidence we used Bayesian Belief Networks (BBNs).  ...  theory, fuzzy sets and possibility theory.  ...  The authors are indebted to the support of EPSRC and the encouragement of the Monitoring Officer Dr Dominic Semple.  ... 
doi:10.1049/ip-sen:19984895 fatcat:oubzlg73xnhpdam5tihwusxylq

A Bayesian Network Approach to Estimating Software Reliability of RSG-GAS Reactor Protection System

S. Santoso, S. Bakhri, J. Situmorang
2019 Atom Indonesia  
A Bayesian network (BN) is applied in this research and used to predict the software defect in the operation which represents the software reliability.  ...  The result achieved is valuable for further reliability estimation by introducing new evidence and experience data, and by setting up an appropriate plan in order to enhance software reliability in the  ...  ACKNOWLEDGMENT This research was a part of a research project supported and funded by Center for Nuclear Reactor Technology and Safety of the National Nuclear Energy Agency of Indonesia and the Ministry  ... 
doi:10.17146/aij.2019.775 fatcat:w5ix4nnsdjczpgo6uaansdwvpe

Bayesian Networks for the Evaluation of Complex Systems Availability

El Hassen Ait Mokhtar, Radouane Kara, Alaa Chateauneuf
2014 International Workshop on Verification and Evaluation of Computer and Communication Systems  
Unlike the simple systems, very few methodologies treat the evaluation of the dependability of complex systems, especially those configured as networks, where it is difficult to take into consideration  ...  the different links and factors that can affect the availability and reliability of such systems.  ...  The first section of this paper is reserved to the Bayesian networks and the modelling of complex systems using Bayesian networks.  ... 
dblp:conf/vecos/MokhtarKC14 fatcat:laojpc3f5vfqxpqhvwhhcqx55i

Techniques for Dealing with Uncertainty in Cognitive Radio Networks [article]

Fatima Salahdine
2017 arXiv   pre-print
A cognitive radio system has the ability to observe and learn from the environment, adapt to the environmental conditions, and use the radio spectrum more efficiently.  ...  In the last step, uncertainty can affect the decision of the cognitive radio system, which sometimes can lead to the wrong action.  ...  Bayesian network [18] and Markov network [19] are examples of graphical models. 1) Bayesian Network Bayesian network is used to present knowledge about an uncertain domain and model how intervening  ... 
arXiv:1701.05468v1 fatcat:tf5exhggrfbtfi3uiwzzakqjk4

Techniques for dealing with uncertainty in cognitive radio networks

Fatima Salahdine, Naima Kaabouch, Hassan El Ghazi
2017 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC)  
Bayesian network [18] and Markov network [19] are examples of graphical models. 1) Bayesian Network Bayesian network is used to present knowledge about an uncertain domain and model how intervening  ...  The Bayesian network as a probabilistic method is more efficient and it is the most used to present, reason, and model uncertainty.  ... 
doi:10.1109/ccwc.2017.7868352 dblp:conf/ccwc/SalahdineKG17 fatcat:ueervetohrgptobxxgrmxfk4bm

Probability and agents

M.G. Valtorta, M.N. Huhns
2001 IEEE Internet Computing  
It is appropriate to model the uncer-tainty using probability theory, which has a long history and a clear justification.  ...  At least in the case of designed systems of agents, we should be able to do better! We should not get too pessimistic, however. Probabilistic models can also help us in managing complexity.  ... 
doi:10.1109/4236.968836 fatcat:cvnwwww4arf5jicn67un75of4u

A Bayesian Belief Network for Local Air Quality Forecasting

Tomaso Vairo, Mario Lecca, Elisabetta Trovatore, Andrea Reverberi, Bruno Fabiano
2019 Chemical Engineering Transactions  
This study is focused on the development of a Bayesian network for air quality assessment and aims at offering a pragmatic and scientifically credible approach to modelling complex systems where substantial  ...  The system used for data assimilation, construction and network learning is completely based on an open source statistical processing software.  ...  Acknowledgments The authors would like to acknowledge all the staff of the meteo-hydrological centre of environmental protection agency of Liguria (ARPAL -CFMI), as well as prof.  ... 
doi:10.3303/cet1974046 doaj:1515e1d7efd9427b8c4c9deaa34c23fc fatcat:zusdnpkeangatlvwbvwu4onwri
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