Predictive Maintenance in Telecom: Artificial Intelligence for predicting and preventing network failures, reducing downtime and maintenance costs, and maximizing efficiency

Abstract

Predictive maintenance (PdM), leveraging Artificial Intelligence (AI), is transforming the telecommunications industry by enabling the prediction and prevention of network failures. This proactive strategy reduces network outages and maintenance costs while enhancing overall system performance. By employing AI technologies such as machine learning algorithms, big data analytics, and sensor data analysis, telecom operators can identify patterns and anomalies indicative of potential component failures. AI-driven models continuously monitor network health, facilitating highly accurate failure predictions and enabling timely interventions. This article examines the application of AI for PdM within the telecom sector, focusing on its impact on operational efficiency, resource optimization, and service stability. The findings highlight significant cost reductions and operational improvements achievable with PdM systems. Furthermore, the paper discusses implementation challenges and key considerations for transitioning to these systems. The future outlook for telecom PdM suggests a continued evolution towards more automated, seamless network management and an improved customer experience.