Author(s) :

Shrestha Shukla.

Volume/Issue :

Abstract :

The integration of Artificial Intelligence (AI), Data Science, and big data technologies is revolutionizing predictive maintenance in the manufacturing sector. This study investigates the role of IoT-enabled sensors, machine learning models, and advanced analytics in optimizing equipment reliability and operational efficiency. Leveraging technologies such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and YOLOv5, the proposed system predicts failures, detects anomalies, and minimizes downtime. By addressing challenges related to real-time data processing, model scalability, and integration costs, this research outlines a comprehensive, phased approach to implement predictive maintenance. The findings demonstrate significant improvements in cost efficiency, safety, and scalability, positioning AI-driven predictive maintenance as a cornerstone for the future of Industry 4.0.

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