Bayesian MCMC updating of a maglev vehicle/guideway system for SHM-based digital twin development

Adeagbo, Mujib Olamide, Wang, Su-Mei, Hao, Shuo and Ni, Yi-Qing 2025. Bayesian MCMC updating of a maglev vehicle/guideway system for SHM-based digital twin development. Journal of Sound and Vibration 618 (PartA) , 119340. 10.1016/j.jsv.2025.119340 Item availability restricted.

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Abstract

Advanced rail transit systems, like maglev, have unique safety and performance requirements due to their innovative engineering. To address these needs, implementing Structural Health Monitoring (SHM) schemes that provide real-time condition information is essential. A promising approach for SHM in maglev trains is digital twinning (DT), which facilitates lifetime prediction and monitoring of system behaviours. As DTs evolve alongside the real structure, continuous updating and recalibration of the virtual twin based on real system characteristics are crucial. This paper presents a methodology for updating the maglev vehicle/guideway system model with the end goal of developing a DT for SHM. The system model is updated using system modal parameters obtained through operational modal analysis. To ensure continuous system recalibration, we propose a real-time multi-step strategy, incorporating both the 2D and 3D system models to simulate the interaction between the maglev vehicle and the guideway. Considering uncertainties in operational conditions, modal parameters are extracted from field data using the fast Bayesian FFT method. The Bayesian-Markov Chain Monte Carlo (MCMC) methodology is employed for model updating to address uncertainties from system complexity. Additionally, a new algorithm is introduced for optimal MCMC convergence. Results demonstrate high accuracy in model updating based on field vibration data, indicating a strong correlation between the developed system models (a fundamental component of the SHM-DT) and the physical system. The updated parameters, such as stiffness and mass, yield valuable insights into the system’s operational conditions. This research enhances digital twinning techniques for maglev systems, providing important strategies for health monitoring.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Additional Information:	License information from Publisher: LICENSE 1: Title: This article is under embargo with an end date yet to be finalised.
Publisher:	Elsevier
ISSN:	0022-460X
Date of First Compliant Deposit:	24 July 2025
Date of Acceptance:	14 July 2025
Last Modified:	06 Aug 2025 12:30
URI:	https://orca.cardiff.ac.uk/id/eprint/180028

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