A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies

A Thelen, X Zhang, O Fink, Y Lu, S Ghosh… - Structural and …, 2022‏ - Springer
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …

[HTML][HTML] Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms

F Naseri, S Gil, C Barbu, E Cetkin, G Yarimca… - … and Sustainable Energy …, 2023‏ - Elsevier
Transportation electrification has been fueled by recent advancements in the technology
and manufacturing of battery systems, but the industry yet is facing serious challenges that …

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

V Nemani, L Biggio, X Huan, Z Hu, O Fink… - … Systems and Signal …, 2023‏ - Elsevier
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

[HTML][HTML] The advance of digital twin for predictive maintenance: The role and function of machine learning

C Chen, H Fu, Y Zheng, F Tao, Y Liu - Journal of Manufacturing Systems, 2023‏ - Elsevier
The recent advance of digital twin (DT) has greatly facilitated the development of predictive
maintenance (PdM). DT for PdM enables accurate equipment status recognition and …

Digital twins in safety analysis, risk assessment and emergency management

E Zio, L Miqueles - Reliability Engineering & System Safety, 2024‏ - Elsevier
Digital twins (DTs) represent an emerging technology that is currently leveraging the
monitoring of complex systems, the implementation of autonomous control systems, and …

Towards a digital twin framework in additive manufacturing: Machine learning and bayesian optimization for time series process optimization

V Karkaria, A Goeckner, R Zha, J Chen, J Zhang… - Journal of Manufacturing …, 2024‏ - Elsevier
Laser directed-energy deposition (DED) offers notable advantages in additive
manufacturing (AM) for producing intricate geometries and facilitating material functional …

Physics-informed machine learning in prognostics and health management: State of the art and challenges

D Weikun, KTP Nguyen, K Medjaher, G Christian… - Applied Mathematical …, 2023‏ - Elsevier
Prognostics and health management (PHM) plays a constructive role in the equipment's
entire life health service. It has long benefited from intensive research into physics modeling …

[HTML][HTML] Revam** structural health monitoring of advanced rail transit systems: A paradigmatic shift from digital shadows to digital twins

MO Adeagbo, SM Wang, YQ Ni - Advanced Engineering Informatics, 2024‏ - Elsevier
Advanced rail transit systems (ARTS), including high-speed rail and maglev trains, provide
enhanced transportation options to meet the growing demand for efficient transportation …

A Physics-Constrained Bayesian neural network for battery remaining useful life prediction

DA Najera-Flores, Z Hu, M Chadha, MD Todd - Applied Mathematical …, 2023‏ - Elsevier
In order to predict the remaining useful life (RUL) of lithium-ion batteries, a capacity
degradation model may be developed using either simplified physical laws or machine …

A Bayesian framework for digital twin-based control, monitoring, and data collection in wireless systems

C Ruah, O Simeone… - IEEE Journal on Selected …, 2023‏ - ieeexplore.ieee.org
Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms
are increasingly seen as a promising paradigm to control, monitor, and analyze software …