AI‐Driven Predictive Maintenance for Biomanufacturing Equipment

In biomanufacturing, the availability of equipment and downtime is crucial to product quality, regulatory and process efficiency. In this chapter, the current interest in predictive maintenance (PM) of biomanufacturing equipment through the application of artificial intelligence (AI) is discussed. Compared to more conservative maintenance practices based upon time-based schedules or failure-based response, AI-based PM works with data analytics and machine learning to predict when machines will fail by leveraging real-time data, sensor integration, and machine learning algorithms. The chapter describes the technical underpinnings of AI based maintenance, such as data ingestion systems, anomaly detection, condition monitoring and predictive modeling. The focus is on the application of these tools within the bioprocessing unit like bioreactors, centrifuges, pumps and filtration systems. Case studies presented in the real world show how AI-enabled PM platforms are being implemented and included an increase in the operational uptime, cost reduction, compliance with regulatory guidelines. Issues like data quality, interconnection with current Supervisory Control and Data Acquisition (SCADA) systems and interpretability of the models also form part of the discussion. Future trends such as digital twins and edge computing are mentioned at the end of the chapter, along with the possibility to continue revolutionizing biomanufacturing predictive maintenance even further. The chapter is intended to be of use to engineers, data scientists and biomanufacturing leaders aiming to increase their operation resilience by deploying intelligent maintenance systems.