AI Predictive Maintenance Prevents Batch Loss And Production Shutdown

In pharmaceutical manufacturing, maintaining sterility and uninterrupted operations is critical, particularly for equipment like Water for Injection (WFI) pumps and Air Handling Units (AHUs). Failures in these systems can lead to costly batch losses—especially when dealing with expensive biologics—or unexpected production shutdowns. To address this risk, JHS implemented a predictive maintenance program using 100 wireless vibration sensors equipped with AI capabilities. These sensors continuously monitor the health and performance of WFI pumps and AHUs, collecting real-time data to establish operational baselines.

AI algorithms analyze these data streams alongside insights from experienced subject matter experts to detect deviations from normal performance patterns. When anomalies are identified, maintenance teams receive alerts, allowing them to address issues before they escalate into critical failures.

This proactive approach proved effective when, following a scheduled maintenance shutdown, one WFI pump exhibited excessive velocity upon restart. The AI system quickly flagged the abnormal reading, prompting the reliability engineering team to investigate. They discovered a misaligned coupling, which was promptly corrected before the pump returned to production. By intervening early, the team avoided potential unplanned downtime and expensive product losses.

The case highlights how AI-human collaboration, machine learning-driven pattern recognition, and targeted interventions can significantly reduce operational risks in pharmaceutical production. This integration of advanced monitoring tools with expert decision-making not only safeguards equipment but also ensures production continuity, minimizes waste, and protects high-value assets from preventable failures.