American International Journal of Research and Innovation

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Predictive Maintenance Models Using Artificial Intelligence Technologies

Author(s) Benoît Iung
Country France
Abstract Predictive maintenance has emerged as an important industrial strategy for improving equipment reliability, reducing operational downtime, and optimizing maintenance activities through intelligent data analysis. The integration of artificial intelligence technologies, including machine learning, deep learning, artificial neural networks, and industrial analytics, has significantly enhanced the capability of predictive maintenance models to forecast equipment failures and support proactive decision-making. This conceptual research article examines artificial intelligence-based predictive maintenance models and their contribution to modern industrial systems.
The study explores how AI technologies enable condition monitoring, failure prediction, resource optimization, and improved asset management across manufacturing and industrial environments. A conceptual research methodology is adopted through the analysis of existing scholarly literature, artificial intelligence frameworks, industrial maintenance models, and smart manufacturing approaches. The findings indicate that AI-driven predictive maintenance provides significant advantages through improved accuracy, reduced maintenance costs, enhanced operational efficiency, and increased equipment lifecycle performance.
However, challenges related to data quality, model complexity, cybersecurity, implementation costs, and technical expertise remain important barriers. The study concludes that artificial intelligence-based predictive maintenance models represent a significant pathway toward intelligent, reliable, and sustainable industrial operations.
Keywords Predictive Maintenance; Artificial Intelligence; Machine Learning; Deep Learning; Industrial Analytics; Smart Manufacturing; Condition Monitoring.
Field Engineering
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-03
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