INTEGRATING ARTIFICIAL INTELLIGENCE AND MATHEMATICAL MODELS FOR PREDICTIVE MAINTENANCE IN INDUSTRIAL SYSTEMS
Keywords:
Integrating, Artificial Intelligence, Mathematical Models, Predictive Maintenance, Industrial SystemsAbstract
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.
Published
How to Cite
Issue
Section
FUDMA Journal of Sciences
How to Cite
Most read articles by the same author(s)
- Justin Onyarin Ogala, Ohoriemu Blessing Okeoghene, COMPUTER SIMULATIONS AS A TOOL FOR ENHANCING ALGEBRA RETENTION IN JUNIOR SECONDARY SCHOOLS: AN ANALYSIS , FUDMA JOURNAL OF SCIENCES: Vol. 8 No. 4 (2024): FUDMA Journal of Sciences - Vol. 8 No. 4
- Justin Onyarin Ogala, Ohoriemu Blessing Okeoghene, A HYBRID APPROACH TO SOLVING COMPLEX OPTIMIZATION PROBLEMS USING EVOLUTIONARY ALGORITHMS AND MATHEMATICAL MODELING , FUDMA JOURNAL OF SCIENCES: Vol. 8 No. 3 (2024): FUDMA Journal of Sciences - Vol. 8 No. 3 (Special Issue)