Neural network-based fiber optic cable fault prediction study for power distribution communication network

Abstract

As the foundation of communication networks, optical fiber carries huge network traffic, so the prediction of fiber optic cable faults is an important guarantee for the operation of communication networks. Based on the combination of fiber optic system networking technology and network management data, this study constructs an alarm correlation analysis method by using data mining technology to obtain the data set of the fault prediction model for the problem of low fault prediction accuracy of traditional communication networks. The dataset is used to balance the sample data by generating a small number of new samples through the generative adversarial network. The memory-based feature generation convolutional network is proposed to enhance the feature interaction to realize fault prediction in communication networks. The prediction model has a high prediction accuracy of 98.68%, which saves about 160 min for repair work through the application of fiber optic cable fault prediction, which compares well with other models. Fault prediction based on neural networks can provide assistance in the operation and maintenance of distribution communication networks.

Keywords

• generative adversarial networks
• feature interaction
• alarm correlation
• distribution communication networks
• fault prediction
• 68m12