Design and Implementation of an AI-Driven Predictive Maintenance Framework for Smart Manufacturing Systems Using IoT Sensor Data and Deep Learning Models
The advancement of Industry 4.0 has led to the widespread adoption of intelligent manufacturing
systems, necessitating efficient maintenance strategies to ensure operational reliability and minimize
downtime. Traditional maintenance approaches are often inadequate due to their reactive or schedule based
nature, which fails to capture real-time equipment conditions. This study presents an AI-driven
predictive maintenance framework that integrates IoT sensor data with deep learning techniques for
early fault detection in industrial machinery. The proposed system utilizes multi-sensor time-series data,
including parameters such as vibration, temperature, and pressure, and applies preprocessing techniques
to enhance data quality. A Long Short-Term Memory (LSTM) network is employed to model temporal
dependencies and predict potential equipment failures with high accuracy. Experimental evaluation
demonstrates that the model achieves strong performance with balanced accuracy, precision, recall, and
F1-score, indicating its robustness and reliability in predictive maintenance applications. The results
highlight the effectiveness of combining IoT and deep learning for proactive maintenance, enabling
improved decision-making and reduced operational costs in smart manufacturing environments.
