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Cyber-Physical Systems (CPSs), e.g., elevator systems and autonomous drivingsystems, are progressively permeating our everyday lives. To ensure theirsafety, various analyses need to be conducted, such as anomaly detection andtime-to-event analysis (the focus of this paper). Recently, it has been widelyaccepted that digital Twins (DTs) can serve as an efficient method to aid inthe development, maintenance, and safe and secure operation of CPSs. However,CPSs frequently evolve, e.g., with new or updated functionalities, which demandtheir corresponding DTs be co-evolved, i.e., in synchronization with the CPSs.To that end, we propose a novel method, named PPT, utilizing anuncertainty-aware transfer learning for DT evolution. Specifically, we firstpretrain PPT with a pretraining dataset to acquire generic knowledge about theCPSs, 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 bothelevator and ADSs case studies, on average, outperforming a baseline method by7.31 and 12.58 in terms of Huber loss, respectively. The experiment resultsalso affirm the effectiveness of transfer learning, prompt tuning anduncertainty quantification in terms of reducing Huber loss by at least 21.32,3.14 and 4.08, respectively, in both case studies.

Pretrain, Prompt, and Transfer: Evolving Digital Twins for Time-to-Event Analysis in Cyber-physical Systems