Real-Time OEE Monitoring with Predictive Analysis for Industrial Inspection Systems

This paper presents a novel system for real-time monitoring of industrial inspection machines that integrates Overall Equipment Effectiveness (OEE) computation with predictive analytics and intelligent alerting. The proposed architecture processes live position and operational data from edge devices, employing a sliding window technique for continuous OEE calculation rather than traditional fixed-interval methods. The system incorporates ARIMA modeling for short-term OEE forecasting and Isolation Forest algorithms for anomaly detection, enabling early identification of performance issues before they escalate into critical failures. A context-aware alerting framework provides maintenance personnel with actionable diagnostic information and recommended interventions. Experimental results from both simulated environments and a six-week deployment at a semiconductor inspection facility demonstrate significant operational improvements, including an 11.4% increase in OEE, 27% reduction in unplanned downtime, and 29% decrease in maintenance resolution time. The system successfully detected 93% of performance anomalies before they affected quality, with a median early detection time of 47 minutes. This work advances the state of predictive maintenance for smart manufacturing environments by transforming retrospective OEE analysis into proactive performance optimization.