Predicting Device Faults in Telecom Using Real-Time Streaming, Cloud Technologies, and Machine Learning

Abstract

This article presents a comprehensive framework for predicting device faults in telecommunication networks using real-time streaming, cloud technologies, and machine learning approaches. The article explores the integration of advanced analytics with traditional network maintenance strategies to create a proactive fault detection system. By leveraging multiple data sources, including device telemetry, historical failure records, and environmental factors, the system enables early detection and prevention of potential network issues. The framework encompasses various components, from robust data foundation and real-time processing pipelines to sophisticated machine learning models and operational monitoring systems. The implementation demonstrates significant improvements in operational efficiency, cost reduction, and service quality enhancement across telecom networks. By combining automated feature engineering, anomaly detection, and continuous model improvement, the system provides telecom operators with powerful tools for maintaining network reliability and optimizing resource allocation. This article contributes to the evolving field of predictive maintenance in telecommunications, offering insights into scalable solutions for modern network management challenges.