A dynamic fault optimization detection method for cable lines based on Bayesian inference and spectral clustering

The cable is a multi-layer structure, and when mixed faults occur, the cable status signal will experience attenuation and distortion. It is necessary to cluster data of different fault types into different clusters, otherwise the packet loss rate will be high and the accuracy will be low. Therefore, a dynamic fault optimization detection method for cable lines based on Bayesian inference and spectral clustering is proposed.Based on the fuzzy Bayesian theory, a scoring function for the operation efficiency of cable lines is constructed, which integrates the characteristic index information of different operating states to analyze the abnormal fuzzy state of cable line data and obtain smooth cable state signals. Based on spectral theory, classify the core point set of cable line data, transform the clustering problem of the core point set into an undirected graph multi-path partitioning problem, complete spectral clustering of cable line data, and control packet loss rate.Using particle swarm optimization algorithm with inertial particles, obtain spectral clustering results with typical dynamic fault characteristics of cable lines, and complete dynamic fault optimization detection of cable lines. The experimental results show that the research method has a very obvious smoothing effect on the original cable status signal, and the clustering results of cable fault data features are very ideal. When completing cable fault detection, the packet loss rate and AUC index are both more ideal.