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The integration of AI into predictive maintenance systems marks a significant advancement in manufacturing technology. By leveraging ML and DL algorithms, manufacturers can transition from scheduled maintenance to condition-based maintenance, optimizing operational efficiency and reducing costs. However, for successful implementation, organizations must address the challenges of data quality and model interpretability. Future work should focus on developing robust hybrid models that combine the physical knowledge of mechanical systems with the pattern-recognition power of AI.

This paper explores the transformative role of Artificial Intelligence (AI) in the domain of predictive maintenance (PdM) for modern manufacturing environments. As Industry 4.0 progresses, traditional time-based maintenance strategies are becoming obsolete due to inefficiency and cost. This study reviews current AI-driven methodologies, including Machine Learning (ML) and Deep Learning (DL) algorithms, utilized for forecasting equipment failure. The paper concludes that while AI integration significantly reduces downtime and operational costs, challenges regarding data quality and model interpretability remain significant hurdles for widespread adoption. asme format citation

Despite the potential benefits, several challenges hinder the universal adoption of AI in PdM. The integration of AI into predictive maintenance systems

ASME uses a numbered citation style. References are numbered in the order they appear in the text, and the same number is used each time that source is cited. Future work should focus on developing robust hybrid

[3] Chen, W., 2020, “Design of a Small Wind Turbine,” Proceedings of the ASME 2020 Power Conference, July 19–23, Virtual Online, Paper No. POWER2020-1642, pp. V001T08A009.

: Sources are listed at the end of the document in the numerical order they were first cited, not alphabetically.