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The concept of 'digital twins' has gained significant traction in industrial engineering, referring to virtual replicas of physical systems that are continuously updated with real-time data. These models enable predictive maintenance by simulating potential failures before they occur, thereby reducing downtime and operational costs. However, the effectiveness of digital twins hinges on the quality and granularity of the data collected. Sparse or biased data can lead to inaccurate simulations, undermining their reliability. Moreover, the integration of digital twins into legacy infrastructure poses substantial technical challenges, requiring significant investment in sensors and connectivity. Despite these hurdles, proponents argue that the long-term benefits, including enhanced efficiency and extended asset lifespan, justify the initial expenditure, particularly in sectors like aerospace and energy where equipment failures carry high risks.