Artificial Intelligence for Network Fault Prediction and Self-Healing: A Comprehensive Review
Abstract
Modern communication networks must provide continuous availability despite increasingly complex architectures and rapidly changing traffic conditions. Conventional fault-management approaches are largely reactive, detecting failures after service degradation has already occurred. Artificial Intelligence (AI) enables a shift toward predictive and autonomous network maintenance by learning patterns associated with failures, performance degradation, and abnormal behavior. This review examines AI-based approaches for network fault prediction, diagnosis, localization, and automated recovery. Machine learning, deep learning, recurrent models, graph neural networks, and reinforcement learning techniques are systematically analyzed across wired, wireless, cloud, edge, and 5G/6G networks. The review categorizes existing approaches according to fault type, data source, learning paradigm, prediction horizon, and recovery strategy. Particular attention is given to network telemetry, logs, alarms, topology information, and time-series performance data. Challenges involving incomplete observations, rare failures, noisy data, concept drift, explainability, and real-time inference are critically discussed. The paper further explores the integration of AI with digital twins and autonomous network controllers to enable predictive maintenance and self-healing capabilities.
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