APGNN : Alarm Propagation Graph Neural Network for fault detection and alarm root cause analysis

Telecommunication network plays an important role in our daily life. Fault detection and alarm root cause analysis are the keys to ensure the normal operation of the network. To reduce the burden on operators, numerous methods are employed to analyse root cause of faults. However, there still remain a large amount of non-essential or transient alarms after root cause analysis. A simple Rule-based method may help ease the problems. But it needs prior expert knowledge and the diversity of alarm pattern makes the rules redundant and complicated. Moreover, it cannot accurately cover all true faults and need manual methods as complement. In this work, we propose Alarm Propagation Graph Neural Network(APGNN), a novel data-driven propagation -based root cause analysis and fault detection approach.It first associates alarms and extracts root-derived graph based on Bayesian Network. Then it constructs alarm propagation graphs(APG). We refine the repair orders to obtain actual fault information. At last, Graph Neural Network is used to extract features and learn the mapping from APG to the true fault. Our method not only detects the true fault from large volume of original alarms, but also analyses the root cause alarms. We evaluate our approach both on the offline and online environment of the real-world IP Radio Access Network. Experiments show that our model outperforms the state-of-art approach by 4.6% in F1-score on average.

Automated summary

In this study, a data-driven propagation-based root cause analysis and fault detection approach called Alarm Propagation Graph Neural Network (APGNN) is proposed. This method associates alarms with root causes using Bayesian Network and constructs alarm propagation graphs (APG). Our approach not only detects true faults from a large volume of original alarms, but also analyzes root cause alarms.