Cyber-physicalnd systems (CPSs), e.g., elevators and autonomous driving systems, are progressively permeating our everyday lives. To ensure their safety, various analyses need to be conducted, such as anomaly detection and time-to-event analysis (the focus of this paper). Recently, it has been widely accepted that digital Twins (DTs) can be an efficient method to aid in developing, maintaining, and safe and secure operation of CPSs. However, CPSs frequently evolve, e.g., with new or updated functionalities, which demand their corresponding DTs be co-evolved, i.e., in synchronization with the CPSs. To that end, we propose a novel method, named PPT , utilizing an uncertainty-aware transfer learning for DT evolution. Specifically, we first pretrain PPT with a pretraining dataset to acquire generic knowledge about the CPSs, followed by adapting it to a specific CPS with the help of prompt tuning. Results highlight that PPT is effective in time-to-event analysis in both elevator and autonomous driving case studies, on average, outperforming a baseline method by 7.31 and 12.58 in terms of Huber loss, respectively. The experiment results also affirm the effectiveness of transfer learning, prompt tuning, and uncertainty quantification in terms of reducing Huber loss by at least 21.32, 3.14, and 4.08, respectively, in both case studies.

中文翻译：

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预训练、提示和传输：不断发展的数字孪生，用于网络物理系统中的时间到事件分析


网络物理系统（CPS），例如电梯和自动驾驶系统，正在逐渐渗透到我们的日常生活中。为了确保它们的安全，需要进行各种分析，例如异常检测和事件时间分析（本文的重点）。最近，人们普遍认为数字孪生（DT）可以成为帮助开发、维护以及安全可靠运行 CPS 的有效方法。然而，CPS 经常发展，例如，具有新的或更新的功能，这要求其相应的 DT 共同发展，即与 CPS 同步。为此，我们提出了一种名为 PPT 的新方法，利用不确定性感知迁移学习进行 DT 演化。具体来说，我们首先使用预训练数据集对 PPT 进行预训练，以获取有关 CPS 的通用知识，然后借助提示调整将其适应特定的 CPS。结果表明，在电梯和自动驾驶案例研究中，PPT 在事件发生时间分析中都很有效，平均而言，在 Huber 损失方面分别比基线方法高出 7.31 和 12.58。实验结果还证实了迁移学习、即时调整和不确定性量化的有效性，在这两个案例研究中，分别将 Huber 损失减少了至少 21.32、3.14 和 4.08。