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（如电梯系统和自动驾驶系统）正逐渐渗透到我们的日常生活中。为确保其安全性，需要开展多种分析工作，包括异常检测和事件时间分析（本文重点关注方向）。近年来，数字孪生已被广泛视为辅助信息物理系统开发、维护及安全可靠运行的有效方法。然而，信息物理系统经常发生演变（例如新增或更新功能），这就要求其对应的数字孪生能够协同演进，即与信息物理系统保持同步。为此，我们提出一种名为PPT的新方法，通过引入不确定性感知的迁移学习来实现数字孪生的演进。具体而言，我们首先利用预训练数据集对PPT进行预训练，获取关于信息物理系统的通用知识；随后借助提示调优技术，将其适配至特定信息物理系统。实验结果表明，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具有显著效果，其Huber损失平均比基线方法分别降低7.31和12.58。两案例研究同时验证了迁移学习、提示调优及不确定性量化在降低Huber损失方面的有效性——分别至少减少21.32、3.14和4.08。