AI-DRIVEN PREDICTIVE MAINTENANCE: REVOLUTIONIZING TELECOMMUNICATIONS NETWORK MANAGEMENT

Abstract

Emphasizing the change from reactive to proactive maintenance techniques, this article investigates the transforming effect of artificial intelligence-driven predictive maintenance systems in the telecommunications sector. By means of a review of present implementations and industry practices, it is examined how artificial intelligence algorithms interpret network operational data to forecast possible failures, optimize maintenance schedules, and improve network dependability. The integration of machine learning models for pattern detection in network performance measurements, equipment sensor readings, and historical maintenance data is investigated in this work. This article shows that predictive maintenance driven by artificial intelligence greatly lowers running costs, causes less disturbance of services, and increases equipment lifetime. Although stressing the advantages, this article also covers implementation issues, including organizational adaptation needs and data quality issues. The article ends with looking at new developments in predictive maintenance, including edge computing integration and autonomous maintenance systems, so offering ideas on the future direction of telecom network management.