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

TL;DR

This paper introduces PPT, a transfer learning approach with prompt tuning and uncertainty quantification, to evolve digital Twins for time-to-event analysis in cyber-physical systems, improving accuracy and adaptability.

Contribution

It presents a novel method combining pretraining, prompt tuning, and transfer learning for digital Twin evolution in CPSs, enhancing time-to-event analysis accuracy.

Findings

PPT outperforms baseline by 7.31 and 12.58 in Huber loss.

Transfer learning reduces Huber loss by at least 21.32%.

Prompt tuning and uncertainty quantification improve model performance.

Abstract

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.