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Research Article

Advancements in Machine Learning for Predictive Maintenance in Industrial Systems

Authors

  • Thomas Levine

Abstract

Predictive maintenance (PdM) has emerged as a pivotal approach in modern industrial systems, enabling the anticipation and prevention of equipment failures by leveraging advanced data analytics and real-time monitoring. This paper explores recent advancements in the application of machine learning (ML) techniques for predictive maintenance, highlighting their potential to improve operational efficiency, reduce downtime, and extend equipment life. This review emphasizes key machine learning algorithms, their implementation in industrial systems, and the challenges associated with data quality, scalability, and real-time analysis. Finally, the paper discusses future directions and opportunities in the field of predictive maintenance driven by machine learning innovations.

Article information

Journal

International Journal of Science and Technology Innovation

Volume (Issue)

3 (1)

Pages

15-23

Published

2024-03-09

How to Cite

Levine , T. (2024). Advancements in Machine Learning for Predictive Maintenance in Industrial Systems. International Journal of Science and Technology Innovation, 3(1), 15-23. https://doi.org/10.70560/ttq08f21

References

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Keywords:

Predictive maintenance Machine learning Industrial systems Real-time monitoring Operational efficiency