IOT SENSOR FUSION AND PREDICTIVE ANALYTICS FOR INDUSTRIAL MACHINERY MAINTENANCE

Abstract

Industrial machinery maintenance is a critical component of modern manufacturing systems, where unexpected equipment failures can result in production losses, increased operational costs, and reduced productivity. Traditional preventive maintenance approaches often rely on fixed schedules and manual inspections, which may fail to identify early degradation patterns. This paper proposes an IoT Sensor Fusion and Predictive Analytics framework for industrial machinery maintenance by integrating Industrial Internet of Things (IIoT), multi-sensor data fusion, machine learning, cloud computing, edge analytics, and intelligent monitoring systems. The proposed methodology collects real-time equipment information from multiple sensors, analyzes operational patterns, predicts potential failures, and enables proactive maintenance decisions. The framework improves fault detection accuracy, equipment reliability, maintenance efficiency, and production continuity through continuous monitoring and predictive intelligence. Experimental evaluation demonstrates improvements in failure prediction capability, operational efficiency, resource utilization, and maintenance planning. The proposed solution provides a scalable approach for intelligent predictive maintenance in Industry 4.0 manufacturing environments. Keywords— IoT Sensor Fusion, Predictive Analytics, Industrial Machinery Maintenance, IIoT, Machine Learning, Fault Prediction, Smart Manufacturing, Industry 4.0.