EDGE–CLOUD PREDICTIVE MAINTENANCE ARCHITECTURE FOR REAL-TIME INDUSTRIAL EQUIPMENT MONITORING

Abstract

Industrial equipment reliability is a critical factor in modern manufacturing environments, where unexpected failures can result in production losses, operational delays, and increased maintenance costs. Traditional maintenance approaches based on scheduled inspection and reactive repair are insufficient for handling complex industrial systems with dynamic operating conditions. This paper proposes an Edge–Cloud Predictive Maintenance Architecture for Real-Time Industrial Equipment Monitoring by integrating edge computing, cloud analytics, Industrial Internet of Things (IIoT), machine learning, sensor fusion, and intelligent monitoring mechanisms. The proposed architecture distributes computational tasks between edge devices and cloud platforms to achieve low-latency equipment monitoring, realtime anomaly detection, predictive failure analysis, and scalable maintenance optimization. Edge nodes perform immediate data processing and fault identification, while cloud platforms support advanced analytics, model training, and lifecycle management. Experimental evaluation demonstrates improvements in fault prediction accuracy, response efficiency, equipment reliability, maintenance optimization, and operational scalability. The proposed framework provides an intelligent solution for Industry 4.0 predictive maintenance applications. Keywords— Edge Computing, Cloud Computing, Predictive Maintenance, Industrial Equipment Monitoring, IIoT, Machine Learning, Sensor Fusion, Smart Manufacturing.