Predictive Maintenance in Manufactuaring with AI and Data Science
Shrestha Shukla
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.