AI-Driven Real-Time Predictive Maintenance System for Industrial Rotating Machinery

Abstract

The increasing dependence on industrial rotating machinery in manufacturing, power generation, and process industries has intensified the need for intelligent maintenance systems capable of preventing unexpected failures and reducing operational downtime. This study presents an AI-driven real-time predictive maintenance system for industrial rotating machinery to enhance equipment reliability, fault diagnosis accuracy, and maintenance efficiency. The proposed framework integrates Industrial Internet of Things (IIoT) sensors, real-time condition monitoring, machine learning algorithms, and predictive analytics to continuously evaluate machinery health under dynamic operational conditions. A data-driven methodology was implemented in which vibration signals, temperature variations, acoustic emissions, rotational speed, and lubrication parameters were continuously collected and analyzed using artificial intelligence-based fault prediction models. Advanced machine learning techniques, including deep learning and anomaly detection algorithms, were employed to identify early-stage faults, estimate remaining useful life, and support proactive maintenance decision-making. Experimental and simulation results demonstrated significant improvements in fault detection accuracy, maintenance scheduling efficiency, equipment availability, and operational reliability compared with conventional preventive maintenance approaches. The AI-driven framework also reduced unplanned downtime, maintenance costs, and energy losses through intelligent real-time monitoring and adaptive fault diagnosis mechanisms. Furthermore, the predictive maintenance system exhibited strong scalability and adaptability for deployment across diverse industrial rotating machinery applications. The study concludes that AI-enabled predictive maintenance technologies provide an effective and scalable solution for enhancing industrial machinery performance, operational safety, and maintenance optimization.