标题： 数字孪生技术在预测性维护中的应用：通过仿真到现实和现实到仿真的迁移实现可迁移性 显示英文标题

标题： Digital Twin Technologies in Predictive Maintenance: Enabling Transferability via Sim-to-Real and Real-to-Sim Transfer

摘要： 物联网（IoT）和人工智能的进步推动了数字孪生（DTs）从概念性想法向更可实现的现实演变。 然而，由于缺乏标准化框架，从学术界到工业界的过渡是复杂的。 本文基于作者之前建立的功能和信息需求，支持标准化的DT开发，重点在于一个关键方面：可迁移性。 尽管现有的DT研究主要集中在资产迁移上，"模拟到现实迁移"和"现实到模拟迁移"——在模拟和现实操作之间转移知识——对于DT的全面生命周期管理至关重要。 此过程中的一个关键挑战是校准"现实差距"，即模拟预测与实际结果之间的差异。 我们的研究调查了将一个现实差距分析（RGA）模块集成到现有DT框架中对有效管理模拟到现实和现实到模拟迁移的影响。 这种集成通过数据管道实现，将RGA模块与DT框架的现有组件连接起来，包括历史存储库和模拟模型。 在卡内基梅隆大学的一座人行天桥上的案例研究展示了我们方法与现有框架不同集成级别的性能。 在RGA模块和完整数据管道完全实施的情况下，我们的方法能够在不牺牲效率的情况下在模拟和现实操作之间进行双向知识迁移。 显示英文摘要

摘要： The advancement of the Internet of Things (IoT) and Artificial Intelligence has catalyzed the evolution of Digital Twins (DTs) from conceptual ideas to more implementable realities. Yet, transitioning from academia to industry is complex due to the absence of standardized frameworks. This paper builds upon the authors' previously established functional and informational requirements supporting standardized DT development, focusing on a crucial aspect: transferability. While existing DT research primarily centers on asset transfer, the significance of "sim-to-real transfer" and "real-to-sim transfer"--transferring knowledge between simulations and real-world operations--is vital for comprehensive lifecycle management in DTs. A key challenge in this process is calibrating the "reality gap," the discrepancy between simulated predictions and actual outcomes. Our research investigates the impact of integrating a single Reality Gap Analysis (RGA) module into an existing DT framework to effectively manage both sim-to-real and real-to-sim transfers. This integration is facilitated by data pipelines that connect the RGA module with the existing components of the DT framework, including the historical repository and the simulation model. A case study on a pedestrian bridge at Carnegie Mellon University showcases the performance of different levels of integration of our approach with an existing framework. With full implementation of an RGA module and a complete data pipeline, our approach is capable of bidirectional knowledge transfer between simulations and real-world operations without compromising efficiency.