Author(s)

Seolhwa Park¹, Lindsay Berry¹, ChanJun Lim^2*

Affiliation(s)

¹Department of Computer Science, University of Copenhagen, Copenhagen 1165, Denmark.

²Department of Computer Science and Engineering, Korea University, Seoul 02841, Republic of Korea.

Corresponding Author

ChangJun Lim

ABSTRACT

In the modern industrial landscape, the integration of predictive maintenance (PdM) using machine learning (ML) has become essential for optimizing plant efficiency and minimizing emissions. This paper explores the transformative potential of predictive maintenance, which leverages data-driven insights to anticipate equipment failures and facilitate timely interventions. By transitioning from traditional maintenance strategies—reactive and preventive—to a proactive approach, organizations can significantly reduce unplanned downtime and enhance operational performance. The study reviews the historical development of predictive maintenance methodologies, highlights current trends in ML applications, and presents case studies demonstrating successful implementations across various industries. The findings reveal that predictive maintenance not only improves equipment reliability and operational efficiency but also contributes to substantial reductions in emissions, thereby promoting sustainable industrial practices. A comprehensive framework for implementing predictive maintenance using machine learning techniques is proposed, emphasizing the importance of data collection, preprocessing, and model development. The paper concludes with a call to action for industries to adopt predictive maintenance solutions, fostering collaboration between academia and industry for future advancements.

KEYWORDS

Predictive maintenance; Machine learning; Emission reduction

CITE THIS PAPER

Seolhwa Park, Lindsay Berry, ChanJun Lim. Predictive maintenance using ML to optimize plant efficiency and reduce emissions. Journal of Manufacturing Science and Mechanical Engineering. 2024, 2(2): 44-51. DOI: 10.61784/msme3008.

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