A Knowledge-Enhanced Transformer-FL Method for Fault Root Cause Localization

Root cause analysis for faults is one of the core tasks in the operation and maintenance of communication networks. Although artificial intelligence techniques can be used to assist manual inspections, fault diagnosis is still a tough problem. In the scenario of fault root cause localization, insufficient associated information makes it difficult to accurately determine the root cause. Meanwhile, it is a big challenge to extract as many feature details as possible from limited information and then fully utilize them. Therefore, this paper proposes a Knowledge-Enhanced Transformer-FL method, namely, KETrans-FL, to address the problem of root cause localization, by treating it as a multi-class classification problem. Our method first constructs a knowledge graph for knowledge enhancement, which consists of four types of nodes (base station, alarm, fault and alarm level) and their relationships based on the network operations. This knowledge enhancement technique incorporates real operation data and other external knowledge (e.g., alarm level) to serve as the source of feature information and thus can extract statistical and embedded features. Then, our method designs a Trans-FL (Transformer-Focal Loss) model, which uses the adapted Transformer encoder to learn the correlation information between input features to generate classification probabilities and employs Focal Loss as the loss function to mitigate severe class imbalance in the multi-class classification problem. Experimental results show that our proposed KETrans-FL method achieves a classification accuracy of nearly 91% and an average AUC score of 93%, indicating a significant improvement on fault root cause localization compared to baseline models. In addition, experimental results also validate the remarkable effect of our knowledge enhancement technique on improving the final classification accuracy.