Lifetime Learning-enabled Modelling Framework for Digital Twin

Recently Digital Twin (DT) has attracted much attention from researchers due to its capacity of system monitoring and health management to improve the reliability and availability of systems. This emerging technology has been considered a promising solution for various sectors to enhance the sustainability of business development. In general, DT relies on the living models for simulating system behaviors to monitor the systems or assets in production. Such models could be either mathematical/physics-based or data-driven, which are able to explain, predict, and describe system behaviors timely and accurately. Therefore, the machine learning-enabled modeling technology has become a powerful tool to develop such data-driven living models. However, data-driven models developed with supervised learning techniques carry a fatal deficiency: once the operational environments are changed, the model may hardly work well or even becomes useless due to the distribution shift between the training data and the new dataset. This paper attempts to address this issue by proposing to apply transfer learning techniques to develop lifetime robust living models for real-world DT systems. This paper presents a framework for developing lifetime data-driven living models. A case study, railway digital twin from our on-going research project along with the preliminary results, will be presented to demonstrate the feasibility and usefulness of the proposed modeling framework for digital twin.