Abstract
<title>Abstract</title> <p>Cement manufacturing depends on extensive rotating machinery operating under severe dust exposure, thermal stress, and continuously varying mechanical loads. Failures in critical assets such as crushers, conveyors, mills, kilns, and separators can result in significant production losses, costly downtime, and reduced operational reliability. Although vibration monitoring remains a widely accepted technique for machinery fault diagnosis, conventional periodic measurement approaches are constrained by limited temporal resolution, delayed fault visibility, and minimal integration with intelligent maintenance decision systems. This paper proposes an IoT enabled vibration monitoring framework specifically designed for cement plant applications. The proposed architecture integrates wireless tri-axial accelerometers, edge computing gateways, cloud-based data infrastructure, and machine-learning-driven diagnostic algorithms to support continuous equipment health assessment and predictive maintenance planning. The framework includes detailed system architecture, signal acquisition methodology, feature extraction pipeline, fault classification strategy, and Remaining Useful Life (RUL) prediction model tailored for harsh cement manufacturing environments. The proposed system is intended to provide a scalable pathway toward early detection in critical plant assets, including crushers, separators, conveyor systems, and grinding mills. By combining edge analytics with cloud intelligence, the framework aims to improve maintenance response time, reduce the risk of catastrophic equipment failure, and support the transition from preventive to predictive maintenance strategies. The paper also discusses implementation considerations, expected operational benefits, and future research directions involving sensor fusion, adaptive learning, and digital twin integration for intelligent cement plant reliability management.</p>