Artificial Intelligence–Based Predictive Maintenance Framework for Industrial Equipment Using IoT Sensor Networks

Abstract

Industrial equipment failure leads to significant operational losses and unplanned downtime in manufacturing environments. Predictive maintenance has emerged as an effective strategy to mitigate such risks by anticipating failures before they occur. This research proposes an Artificial Intelligence–based predictive maintenance framework that integrates Internet of Things sensor networks with machine learning analytics to monitor equipment health in real time. The framework collects operational data including vibration, temperature, acoustic emissions, and electrical current from industrial machines using distributed IoT sensors. Data is transmitted through a low-latency communication architecture and processed using an intelligent analytics pipeline that includes feature extraction, anomaly detection, and predictive modeling. A hybrid machine learning model combining Long Short-Term Memory networks and Random Forest classifiers is employed to predict equipment failure patterns. The proposed system architecture supports edge computing for preliminary analysis and cloud-based platforms for deep learning model training. Experimental evaluation using industrial machinery datasets demonstrates improved prediction accuracy and reduced downtime compared to conventional preventive maintenance strategies. The results indicate that the proposed system can achieve failure prediction accuracy above 92 percent while reducing maintenance costs and improving equipment availability. The research contributes a scalable predictive maintenance framework suitable for modern smart factories and Industry 4.0 environments.