Identifying the root cause of cable network problems with machine learning
This work addresses the challenge of efficiently diagnosing and preventing faults in hybrid fiber coaxial networks, which is crucial for maintaining network connectivity, though it appears incremental in its approach.
The paper tackled the problem of identifying root causes of cable network problems by automating a simple business rule, which improved precision@1 by 2.3 times compared to state-of-the-art machine learning methods, and also evaluated approaches for forecasting network faults to enable predictive maintenance.
Good quality network connectivity is ever more important. For hybrid fiber coaxial (HFC) networks, searching for upstream high noise in the past was cumbersome and time-consuming. Even with machine learning due to the heterogeneity of the network and its topological structure, the task remains challenging. We present the automation of a simple business rule (largest change of a specific value) and compare its performance with state-of-the-art machine-learning methods and conclude that the precision@1 can be improved by 2.3 times. As it is best when a fault does not occur in the first place, we secondly evaluate multiple approaches to forecast network faults, which would allow performing predictive maintenance on the network.