A Literature Review: Application of AI for Predictive Maintenance in Digital Twins Across Various Industries

This paper provides a comprehensive literature review on the integration of Artificial Intelligence (AI) into Digital Twin (DT) systems for predictive maintenance across various industries. As industries adopt digital transformation, Digital Twins have emerged as powerful tools, linking physical assets with their digital counterparts for real-time monitoring, analysis, and diagnostics. By leveraging AI methods like machine learning and deep learning, predictive maintenance within DTs can identify and mitigate potential failures before they disrupt operations. This review synthesizes recent advancements in AI applications within DTs, examining sector-specific methods, applications, and the impact on operational efficiency. We explore challenges such as real-time data processing, model complexity, and system integration, presenting a comparative analysis across industries including manufacturing, healthcare, energy, and transportation.