AI-Driven Predictive Maintenance Optimization Framework (AIPMOF) for Intelligent Network Fault Detection and Infrastructure Reliability Enhancement

Abstract

Modern network infrastructures support mission-critical services across cloud computing, telecommunications, industrial automation, and enterprise information systems. As these infrastructures become increasingly complex, conventional reactive and preventive maintenance strategies struggle to identify hidden performance degradation before service disruptions occur. Artificial Intelligence (AI)-driven predictive maintenance has emerged as an effective solution for forecasting network failures, optimizing maintenance schedules, and improving infrastructure reliability through continuous analysis of operational data. This paper proposes the AI-Driven Predictive Maintenance Optimization Framework (AIPMOF), an intelligent framework that integrates data acquisition, preprocessing, feature engineering, machine learning-based fault prediction, reliability assessment, and maintenance decision support into a unified predictive maintenance architecture. The framework employs Support Vector Regression (SVR) and AdaBoost Regression to predict network reliability scores using key operational indicators, including CPU utilization, memory utilization, and network latency. Experimental evaluation was conducted using a dataset containing 200 network performance records. Performance was assessed using multiple regression evaluation metrics, including the coefficient of determination (R²), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Explained Variance Score (EVS), Mean Squared Logarithmic Error (MSLE), and Median Absolute Error (MedAE). The proposed framework demonstrates strong predictive capability, with SVR achieving an R² value of 0.9444 on the testing dataset, outperforming AdaBoost Regression, which achieved an R² value of 0.9195. The results indicate that AI-based predictive maintenance can accurately estimate infrastructure reliability and provide early warnings of potential failures, thereby reducing downtime, maintenance costs, and operational risks. The proposed AIPMOF framework contributes a scalable and intelligent decision-support mechanism suitable for cloud infrastructures, software-defined networks, enterprise systems, and Industrial Internet of Things (IIoT) environments. The framework provides a foundation for future autonomous, self-healing network management systems capable of continuously learning from operational data and adapting maintenance strategies in real time.