Spectral Graph Wavelet based Component Clustering for System Reliability Analysis

Components symmetry commonly exists in complex systems, which usually leads to considerable redundant computations in reliability analysis. Mining and making use of symmetry information can improve computational efficiency and reduce computational cost that is related to reliability analysis of complex systems. However, few literatures provide attention to this issue in reliability area. This paper proposes a graph learning based method to measure the similarity among the components' local topological structure in a system. Based on the learned structural role similarity, components are clustered into different groups to reduce the complexity of the system. Application of the proposed method is presented in reducing computation effort of system survival signature, and a nearly 77% decrease of computation times demonstrates the effectiveness of the proposed method.