Artificial Intelligence-Based Predictive Maintenance Framework for Smart Manufacturing in Industry 4.0
Abstract
Unplanned equipment failures significantly impact manufacturing productivity and operational costs in Industry 4.0 environments. Traditional maintenance strategies often rely on periodic inspections that fail to predict unexpected machine failures. This paper presents an artificial intelligence-based predictive maintenance framework that combines Internet of Things (IoT) sensors, digital monitoring, and ensemble machine learning techniques to forecast equipment degradation. Real-time sensor data, including vibration, temperature, pressure, and acoustic emissions, are analyzed using Extreme Gradient Boosting (XGBoost), Random Forest, and Long Short-Term Memory (LSTM) networks. Feature selection and anomaly detection modules improve prediction accuracy while minimizing false alarms. Experimental evaluation demonstrates enhanced fault detection capability, reduced maintenance costs, and increased machine availability. The proposed framework supports intelligent manufacturing systems by enabling proactive maintenance scheduling and improving industrial productivity.
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