Leveraging Machine Learning for Predictive Maintenance in Pharmaceutical Production

In the pharmaceutical manufacturing industry, predictive maintenance (PdM) is an evolving approach that employs machine learning (ML) integrated with IoT data collection to mitigate equipment malfunctions and improve operational efficiency. This paper examines how ML algorithms can foresee failures prior to their occurrence, thereby reducing downtime and ensuring compliance with rigorous industry standards. Essential methods include anomaly detection and deep learning techniques for forecasting maintenance needs. By embracing a robust predictive maintenance strategy, pharmaceutical manufacturers can significantly reduce costs, boost productivity, and maintain high production quality standards.