Effects of redundant systems on controlling the disaster spreading in networks

Ouyang, Min; Qi, Fei; Yu, Miao; Wang, Gaoxia; Luan, En-Jie

doi:10.1016/j.simpat.2008.09.013

Cited by 16 publications

(3 citation statements)

References 13 publications

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“…Redundant systems are widely used in aerospace, nuclear power, train control system, and other high-risk fields. [1][2][3] Due to the restraint of complex structure and expansion, reliability test of redundant system is difficult, reliability prediction of redundant system is generally researched in probabilistic inference view. According to the failure distributions of units and the logical structure, the system's reliability is calculated.…”

Section: Introductionmentioning

confidence: 99%

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Reliability prediction used dynamic Bayesian networks for redundant system with time‐lag correlation

Liu

Zhou

Jia

et al. 2022

Quality & Reliability Eng

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In the reliability prediction process of a redundant system, active units and standby units are assumed independent generally. Due to the interaction among units, failure of the active unit gives the standby unit a potential interior shock, then the standby unit fails after a period of operation. Therefore, the correlation between the active unit and the standby unit is triggered by the failure of the active unit and also has time‐lag. In this paper, a correlation estimated method with two parameters is proposed based on cross‐correlation analysis, where the two parameters are time‐lag interval and time‐lag correlation coefficient, the discriminant conditions of independent, simultaneous correlation and time‐lag correlation are given. Subsequently, the Conditional Probability Table (CPT) of Dynamic Bayesian Network (DBN) is updated by adding time‐lag intervals and time‐lag correlation coefficients. Then, DBN is optimized into Time‐lag Dynamic Bayesian Network (TDBN). Meanwhile, the modeling process and calculated method of TDBN are given. To reduce the complexity of TDBN, the maximum time‐lag correlation coefficient and the time‐lag interval corresponding are regarded as the main parameters to estimate time‐lag correlation. Finally, through the simulation analysis and the case of Chinese train control system (CTCS‐3), it could be seen that the reliability prediction of TDBN is more accurate than DBN, TDBN improves the reliability prediction accuracy of train and driver interface module to 0.8135 either.

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Section: Introductionmentioning

confidence: 99%

“…3, there is no correlation in the root node of DBN. By analyzing the cross-correlation function, it is found that time-lag relationships between root nodes exists.…”

mentioning

confidence: 98%

Reliability prediction used dynamic Bayesian networks for redundant system with time‐lag correlation

Liu

Zhou

Jia

et al. 2022

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…on network disaster spreading by using weights neural network model , and discussed the effectiveness of six coping strategies and optimal strategy in detail. A lot of control and prevention strategies for cascade behavior have been proposed on how to get the best ability to resist cascade reaction by limited resources [3,4,5], OuYang and others [6] study the cascade dynamics of a complex system with redundant resources by using the node dynamics equations in response to disasters and accidents in infrastructure networks, and compare the effectiveness of different resource allocation strategies respectively.…”

Section: Introductionmentioning

confidence: 99%

Research on Defense Model of Cascading Failures in Complex Information System Networks

Chen¹,

Li²

2015

Proceedings of the 2015 International Conference on Education, Management, Information and Medicine

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Abstract. With the improvement of social information network, the information infrastructure confronts with increasing security threats. Governments and security groups are reinforcing research on the complex information systems vulnerabilities and especially the network vulnerabilities. Among them, the defense of network cascading failures is one of the most important safety issues in complex network. This paper made a research on the defense mechanism against cascading collapse effect in communication networks and proposed a defense model based on emergency flow-limiting mechanism. And by using three sample networks in simulation experiments, the plan has been proved working well in controlling and defensing cascading collapse phenomenon appearing in communication networks. At the same time, we conducted some simulating researches and analyses on defense plan of deleting the modules with lower centralization degree. By comparison, we find defense model with emergency flow-limiting mechanism has a wider application scope and higher efficiency, while costing less, providing prominent theoretical support to prevention and control of network cascading failures.

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Leveraging Network Theory and Stress Tests to Assess Interdependencies in Critical Infrastructures

Galbusera

Giannopoulos

2019

Advanced Sciences and Technologies for Security Applications

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Effects of redundant systems on controlling the disaster spreading in networks

Cited by 16 publications

References 13 publications

Reliability prediction used dynamic Bayesian networks for redundant system with time‐lag correlation

Reliability prediction used dynamic Bayesian networks for redundant system with time‐lag correlation

Research on Defense Model of Cascading Failures in Complex Information System Networks

Leveraging Network Theory and Stress Tests to Assess Interdependencies in Critical Infrastructures

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