Leveraging Machine Learning for Predictive Maintenance in Pharmaceutical Production

Authors: Ravi Kiran Koppichetti

Country: United States

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Abstract: 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.

Keywords: Predictive maintenance, Internet of things (IoT), Pharmaceutical manufacturing systems, Machine learning, Artificial intelligence, Big data, Industry 4.0, Data mining

Paper Id: 232207

Published On: 2025-03-04

Published In: Volume 13, Issue 2, March-April 2025

Cite This: Leveraging Machine Learning for Predictive Maintenance in Pharmaceutical Production - Ravi Kiran Koppichetti - IJIRMPS Volume 13, Issue 2, March-April 2025.

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