INTEGRATING ARTIFICIAL INTELLIGENCE AND MATHEMATICAL MODELS FOR PREDICTIVE MAINTENANCE IN INDUSTRIAL SYSTEMS

Authors

Keywords:

Integrating, Artificial Intelligence, Mathematical Models, Predictive Maintenance, Industrial Systems

Abstract

Predictive maintenance is a critical task for ensuring the reliability and efficiency of industrial systems. The integration of artificial intelligence (AI) and mathematical models has shown great potential in improving the accuracy and efficiency of predictive maintenance. This study provides an overview of the different types of mathematical models used for predictive maintenance, including physics-based, data-driven, and hybrid models. The study also discusses how AI techniques, such as machine learning and deep learning, can be used to enhance the accuracy and efficiency of predictive maintenance models. Additionally, the article highlights some of the challenges and limitations of integrating AI and mathematical models for predictive maintenance in industrial systems. Finally, this study provides a case study to demonstrate the practical application of the integrated approach for predictive maintenance in an industrial setting. This article aims to provide a comprehensive overview of the state-of-the-art in integrating AI and mathematical models for predictive maintenance and to provide guidance for researchers and practitioners working in this field.

Dimensions

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Published

30-06-2024

How to Cite

INTEGRATING ARTIFICIAL INTELLIGENCE AND MATHEMATICAL MODELS FOR PREDICTIVE MAINTENANCE IN INDUSTRIAL SYSTEMS. (2024). FUDMA JOURNAL OF SCIENCES, 8(3), 501-505. https://doi.org/10.33003/fjs-2024-0803-2593

Issue

Vol. 8 No. 3 (2024): FUDMA Journal of Sciences - Vol. 8 No. 3 (Special Issue)

Section

Research Articles
Copyright & Licensing

How to Cite

INTEGRATING ARTIFICIAL INTELLIGENCE AND MATHEMATICAL MODELS FOR PREDICTIVE MAINTENANCE IN INDUSTRIAL SYSTEMS. (2024). FUDMA JOURNAL OF SCIENCES, 8(3), 501-505. https://doi.org/10.33003/fjs-2024-0803-2593

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