Prediction of Cascading Failures in Spatial Networks.
Cascading overload failures are widely found in large-scale parallel systems and remain a major threat to system reliability; therefore, they are of great concern to maintainers and managers of different systems. Accurate cascading failure prediction can provide useful information to help control networks. However, for a large, gradually growing network with increasing complexity, it is often impractical to explore the behavior of a single node from the perspective of failure propagation. Fortunately, overload failures that propagate through a network exhibit certain spatial-temporal correlations, which allows the study of a group of nodes that share common spatial and temporal characteristics. Therefore, in this study, we seek to predict the failure rates of nodes in a given group using machine-learning methods. We simulated overload failure propagations in a weighted lattice network that start with a center attack and predicted the failure percentages of different groups of nodes that are separated by a given distance. The experimental results of a feedforward neural network (FNN), a recurrent neural network (RNN) and support vector regression (SVR) all show that these different models can accurately predict the similar behavior of nodes in a given group during cascading overload propagation.
School of Reliability and Systems Engineering, Beihang University, Beijing, China.
Yang
Y
Shunkun
School of Reliability and Systems Engineering, Beihang University, Beijing, China.
Zhang
http://orcid.org/0000-0001-9179-9425
Z
Jiaquan
School of Reliability and Systems Engineering, Beihang University, Beijing, China.
Lu
L
Dan
D000465:Algorithms; D003198:Computer Simulation; D000069550:Machine Learning; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results