Edge-AI Enabled Predictive Maintenance for CNC Machines Using Real-Time IoT Sensor Analytics

Abstract

CNC (Computer Numerical Control) machines are central to modern manufacturing processes, offering high precision, repeatability, and productivity. However, their unplanned failures can severely impact throughput and operational cost. Conventional maintenance strategies—reactive and time-based preventive—are inadequate in dynamic industrial environments. Moreover, existing cloud-based predictive systems suffer from latency, network dependency, and privacy challenges. This work presents an Edge-AI-based predictive maintenance architecture that locally analyzes real-time IoT sensor data from CNC machines using compact AI models deployed on Raspberry Pi edge devices. The proposed system employs vibration, temperature, and current sensors to collect equipment health data and utilizes algorithms such as Isolation Forest and Long Short-Term Memory (LSTM) for anomaly detection and Remaining Useful Life (RUL) estimation. Experimental simulation demonstrates real-time fault detection and maintenance scheduling without cloud reliance. The solution promises reduced downtime, enhanced reliability, and cost-efficient manufacturing continuity.