Electrical engineering, Faculty of Polytechnic Sciences, International University of Travnik in Travnik , Travnik , Bosnia and Herzegovina
Electrical engineering, Faculty of Polytechnic Sciences, International University of Travnik in Travnik , Travnik , Bosnia and Herzegovina
Electrical engineering, Faculty of Polytechnic Sciences, International University of Travnik in Travnik , Travnik , Bosnia and Herzegovina
In the rapidly evolving telecommunication world, minimizing downtime and optimizing infrastructure efficiency are critical needs. Artificial intelligence (AI)-enabled predictive maintenance is revolutionizing the way telecom operators perform network asset maintenance and avoidantly repair imminent failures. Through the utilization of machine learning algorithms and real-time analytics, AI-enhanced predictive maintenance enables early fault detection, reduces operational costs, and enhances service uptime. It explains how cutting-edge predictive models are being integrated into telco operations, refers to the role played by big data and IoT in this transition, and highlights the strategic benefits and challenges of using AI in predictive maintenance operations. With increasing complexity in the telecom ecosystem, predictive maintenance comes across as a key enabler of intelligent and more robust networks.
Writing – original draft, A.M.; Writing – review & editing, A.M.; Supervision, G.P. All authors have read and agreed to the published version of the manuscript.
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