EDGE–CLOUD PREDICTIVE MAINTENANCE ARCHITECTURE FOR INTELLIGENT MANUFACTURING EQUIPMENT

Abstract

Intelligent manufacturing systems require advanced maintenance strategies capable of reducing equipment failures, minimizing downtime, and improving operational efficiency. Traditional maintenance approaches are limited by delayed analysis, periodic inspections, and lack of real-time equipment intelligence. This paper proposes an Edge–Cloud Predictive Maintenance Architecture for intelligent manufacturing equipment by integrating edge computing, cloud analytics, Industrial Internet of Things (IIoT), machine learning, sensor fusion, Digital Twin technology, and intelligent monitoring mechanisms. The proposed framework distributes computational tasks between edge devices and cloud platforms to enable low-latency fault detection, real-time equipment monitoring, predictive failure analysis, and scalable model management. Edge nodes perform rapid data processing and anomaly detection, while cloud platforms provide largescale analytics, model training, and lifecycle optimization. Experimental evaluation demonstrates improvements in fault prediction accuracy, response time, equipment reliability, maintenance efficiency, and resource utilization. The proposed architecture provides a scalable and adaptive solution for next-generation Industry 4.0 predictive maintenance environments. Keywords— Edge Computing, Cloud Computing, Predictive Maintenance, Intelligent Manufacturing, IIoT, Machine Learning, Digital Twin, Smart Factory