Predictive Network Fault Detection and Management

This technical report outlines a machine learning-based approach for predictive network fault detection aimed at enhancing the efficiency of Network Operations Centers (NOCs). It begins by discussing the current challenges faced by NOCs, particularly the overwhelming volume of alarms due to the rapid growth of 5G and IoT technologies. The report details the need for a shift from reactive to proactive fault detection methods, emphasizing the importance of machine learning algorithms in predicting network faults before they occur. It elaborates on the process of ticket prioritization and alarm classification, highlighting how a robust ML model can streamline operations and reduce unnecessary service disruptions. Furthermore, the report presents a structured methodology for implementing predictive models, including data refinement techniques and the establishment of strategic threshold points in the alarm lifecycle. The findings indicate significant improvements in operational efficiency, including a notable reduction in costs associated with service disruptions. Overall, the report serves as a comprehensive guide for service providers aiming to enhance their network management capabilities.