A preventive maintenance approach to optimize fault management using machine learning

Abstract

Mobile networks' fault management can take advantage of Machine Learning (ML) algorithms making its maintenance more proactive and preventive. Currently, Network Operations Centers (NOCs) still operate in reactive mode, where the troubleshoot is only done after the problem identification. The network evolution to a preventive maintenance enables the problem prevention or quick resolution, leading to a greater network and services availability, to a better operational efficiency and, above all, ensures customer satisfaction. In this paper, different algorithms for Sequential Pattern Mining (SPM) and Association Rule Learning (ARL) are explored, using real Fault Management (FM) data from a live Long Term Evolution (LTE) network. A comparative analysis of the performance and efficiency between all the algorithms was carried out, having observed a decrease of 3.31% in the total number of alarms and 70.45% in the number of alarms corresponding to the same type. There were also considerable reductions in the number of alarms per network node or zone, identifying 39 nodes that no longer had any unresolved alarm. These results demonstrate that the recognition of sequential alarm patterns allows the preventive maintenance of a mobile communications network.