Cyber-Physical Systems (CPSs), e.g., elevator systems 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 serve as an efficient method to aid in the development, maintenance, 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 ADSs 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.

翻译：信息物理系统（CPS，例如电梯系统和自动驾驶系统）正逐步渗透到我们的日常生活中。为确保其安全性，需要进行各类分析，如异常检测和本文重点关注的生存分析（事件时间分析）。近年来，数字孪生（DT）已被广泛认为是辅助CPS开发、维护及安全可靠运行的有效方法。然而，CPS频繁演进（例如新增或更新功能），要求其对应的DT同步演化（即与CPS协同进化）。为此，我们提出一种名为PPT的新方法，利用面向DT演化的不确定性感知迁移学习。具体而言，我们首先通过预训练数据集对PPT进行预训练，获取关于CPS的通用知识，随后借助提示调优将其适配至特定CPS。结果表明，在电梯系统和自动驾驶系统案例中，PPT在事件时间分析任务上表现优异，其Huber损失相较于基线方法平均分别降低7.31和12.58。实验进一步证实，迁移学习、提示调优与不确定性量化在两个案例中分别至少降低Huber损失21.32、3.14和4.08，验证了其有效性。