Disaster propagation in interdependent networks with different link patterns

Li, Jing; Li, Zequan; Qi, Hui; Zhang, Qiuhan

doi:10.1103/physreve.105.034302

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…Interdependence can be categorized into different types and may exist at different levels, i.e., between systems, subsystems, or components [4]. Under the dependent relationship, the system has a polar failure [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Artime et al [10] studied the impact of different multiplex topological features on the robustness of the system when subjected to non-local cascade propagation. Li et al [11] proposed a model that combines two dynamic mechanisms, i.e., the spreading of failure in layer-dependent networks, where each node in a layer depends on one in another layer.…”

Section: Introductionmentioning

confidence: 99%

“…In order to accurately determine the cascading failure, the interdependence and degree between nodes in the system have become a new research point. Aiming at this problem, some of the literature has studied it [4,9,[11][12][13]. Koosha Marashi et al [4] used correlation measures and heuristic causal analysis to identify the interdependence between components of network physical systems.…”

Section: Introductionmentioning

confidence: 99%

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Failure Propagation Prediction of Complex Electromechanical Systems Based on Interdependence

et al. 2023

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Interdependence is an inherent feature of the cyber-physical system. Small damage to one component in the system may affect several other components, leading to a series of failures, thus collapsing the entire system. Therefore, the system failure is often caused by the failure of one or more components. In order to solve this problem, this paper focuses on a failure propagation probability prediction method for complex electromechanical systems, considering component states and dependencies between components. Firstly, the key component set in the system is determined based on the reliability measure. Considering the three coupling mechanisms of mechanical, electrical, and information, a topology network model of the system is constructed. Secondly, based on the topology network model and fault data, the calculation method of influence degree between components is proposed. Three state parameters are used to express the risk point state of each component in the system through mathematical representation, and the correlation coefficient between the risk point state parameters is calculated and measured based on the uncertainty evaluation. Then, the influence matrix between the system risk points is constructed, and the fault sequence is predicted by using the prediction function of an Artificial Neural Network (ANN) to obtain the fault propagation probability. Finally, the method is applied to the rail train braking system, which verifies that the proposed method is feasible and effective.

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