Towards Industry 4.0: An IoT-Enabled Data-Driven Architecture for Predictive Maintenance in Pharmaceutical Manufacturing

In recent years, there has been a surge in the adoption of Predictive Maintenance (PdM) systems. This is primarily due to their ability to restrict maintenance expenses, reduce unscheduled downtime, and enhance the dependability of equipment. The implementation of Predictive Maintenance (PdM) systems holds significant importance in the pharmaceutical industry, where equipment downtime can have severe implications for product quality and patient safety. The present study puts forth a system architecture for PdM in the pharmaceutical sector, with a particular emphasis on the cyclotron as a case study. Several novel characteristics distinguish our predictive maintenance architecture from prior techniques. We start with advanced machine learning techniques that can handle complex and dynamic cyclotron data patterns. Second, we combine realtime sensor data from many sources for precise health monitoring and fault diagnosis. Our design also uses a data-driven prognostic model to predict breakdowns and suggest maintenance. These innovative parts create a full, proactive cyclotron maintenance system. The implementation of a PdM system has the potential to enhance operational efficiency, ensure safety, and improve product quality by anticipating equipment malfunctions prior to their occurrence. The proposed architecture for predictive maintenance makes a significant contribution to the field of cyclotron maintenance. By leveraging advanced machine learning techniques, real-time sensor data integration, and proactive fault prediction, our solution addresses the critical challenges associated with ensuring cyclotrons operate without interruption. Our research opens up novel possibilities for optimizing predictive maintenance for complex, high-stakes systems such as cyclotrons.