Predictive Maintenance for Industry 4.0 Using IoT and Machine Learning

Industry 4.0 introduces significant challenges in optimizing maintenance processes to enhance operational efficiency and minimize downtime. Traditional maintenance approaches, such as reactive and preventive maintenance, are often inefficient and costly. This study proposes an IoT-driven predictive maintenance architecture to address these challenges, leveraging real-time data from IoT sensors, cloud computing, and machine learning algorithms. The focus of the research is on developing a predictive maintenance framework for industrial equipment, using data collected from milling machines to predict potential failures. Machine learning algorithms, including Logistic Regression, Random Forest, and Decision Trees, were applied to sensor data to identify anomalies and forecast equipment failures. The results show that the proposed system significantly reduces downtime by up to 50% and maintenance costs by 25%, while optimizing maintenance schedules for better resource allocation. This architecture demonstrates the potential of integrating IoT and machine learning in Industry 4.0 to transform traditional maintenance practices into more proactive, cost-effective strategies. The proposed solution offers a scalable framework that can be adapted across various manufacturing sectors, enhancing productivity and minimizing operational disruptions. This research highlights the significant impact of predictive maintenance in fostering smarter, more efficient industrial environments, driving advancements in Industry 4.0.