We propose a synthesis method for the design of digital twins applicable to various systems (pneumatic, hydraulic, electrical/electronic circuits). The methodology allows representing the operation of these systems through an active digital twin, thereby enabling a more suitable and easier computer-aided design, simulation, control, and monitoring. Furthermore, our methodology enables the detection of a system's actions on its own inputs (for example, in pneumatics: backflow of gases trapped in part of a fluidic system onto its own inputs). During the simulation or monitoring phase, the approach also facilitates real-time diagnosis of the controlled system. The outputs, on the controlled physical system or its digital twin, do not depend only on the current inputs but also on the history of the inputs and the history of internal states and variables. In other words, the underlying sequential logic has a memory while an only combinational logic approach does not. These capabilities can contribute to the digital transformation of the factory of the future.
Real-time monitoring of distribution systems has be-come necessary, due to the deregulation of electricity markets and the wide deployment of distributed energy resources. To monitor voltage and current at sub-cycle detail, requires, typically, major investment undertaking and disruptions to the operation of the grid. In this work, measurements of the low voltage (LV) side of distribution transformers (T/F) are used to calculate in real time the waveforms of their medium voltage (MV) sides, based on a mathematical model of said T/F. This model is, essentially, the dig-ital twin of the MV side …
Deep neural networks (DNNs) are often coupled with physics-based models or data-driven surrogate models to perform fault detection and health monitoring of systems in the low data regime. These models serve as digital twins to generate large quantities of data to train DNNs which would otherwise be difficult to obtain from the real-life system. However, such models can exhibit parametric uncertainty that propagates to the generated data. In addition, DNNs exhibit uncertainty in the parameters learnt during training. In such a scenario, the performance of the DNN model will be influenced by the…
A generative adversarial network optimization method for damage detection and digital twinning by deep AI fault learning: Z24 Bridge structural health monitoring benchmark validation
University of Bath（巴斯大学）；Loughborough University（拉夫堡大学）
Queen’s University Belfast（贝尔法斯特女王大学）；National Chiao Tung University (NCTU)（国立交通大学）；National Yang Ming Chiao Tung University (NYCU)（国立阳明交通大学）
While the concept of a digital twin to support maritime operations is gaining attention for predictive maintenance, real-time monitoring, control, and overall process optimization, clarity on its implementation is missing in the literature. Therefore, in this review we show how different authors implemented their digital twins, discuss our findings, and finally give insights on future research directions.
Deep Generative and Discriminative Digital Twin endowed with Variational Autoencoder for Unsupervised Predictive Thermal Condition Monitoring of Physical Robots in Industry 6.0 and Society 6.0
Department of Computer Science \& Engineering, Frankfurt University of Applied Sciences, Frankfurt a.M., Germany (e-mail: ).
Digital twin is a virtual replica of a real-world object that lives simultaneously with its physical counterpart. Since its first introduction in 2003 by Grieves, digital twin has gained momentum in a wide range of applications such as industrial manufacturing, automotive and artificial intelligence. However, many digital-twin-related approaches, found in industries as well as literature, mainly focus on modelling individual physical things with high-fidelity methods with limited scalability. In this paper, we introduce a digital-twin architecture called TiLA (Twin-in-the-Loop Architecture). T…
We demonstrate an end-to-end framework to improve the resilience of man-made systems to unforeseen events. The framework is based on a physics-based digital twin model and three modules tasked with real-time fault diagnosis, prognostics and reconfiguration. The fault diagnosis module uses model-based diagnosis algorithms to detect and isolate faults and generates interventions in the system to disambiguate uncertain diagnosis solutions. We scale up the fault diagnosis algorithm to the required real-time performance through the use of parallelization and surrogate models of the physics-based di…
How to Integrate Digital Twin and Virtual Reality in Robotics Systems? Design and Implementation for Providing Robotics Maintenance Services in Data Centers
University of Twente（特文特大学）；Beijing Hanning Tech Co., Ltd（北京汉宁科技有限公司）
The use of Digital Twins is set to transform the manufacturing sector by aiding monitoring and real-time decision making. For several applications in this sector, the system to be modeled consists of a mix of discrete-event and continuous processes interacting with each other. Building simulation-based Digital Twins of such systems necessitates an open, flexible simulation framework which can support easy modeling and fast simulation of both continuous and discrete-event components, and their interactions. In this paper, we present an outline and key design aspects of a Python-based framework …
The widespread adoption of Electric Vehicles (EVs) is limited by their reliance on batteries with presently low energy and power densities compared to liquid fuels and are subject to aging and performance deterioration over time. For this reason, monitoring the battery State Of Charge (SOC) and State Of Health (SOH) during the EV lifetime is a very relevant problem. This work proposes a battery digital twin structure designed to accurately reflect battery dynamics at the run time. To ensure a high degree of correctness concerning non-linear phenomena, the digital twin relies on data-driven mod…
Manually monitoring water quality is very exhausting and requires several hours of sampling and laboratory testing for a particular body of water. This article presents a solution to test water properties like electrical conductivity and pH with a remote-controlled floating vehicle that minimizes time intervals. An autonomous surface vehicle (ASV) has been designed mathematically and operated via MATLAB \& Simulink simulation where the Proportional integral derivative (PID) controller has been considered. A PVC model with Small waterplane area twin-hull (SWATH) technology is used to develop th…
University of Sheffield（谢菲尔德大学）；Rolls-Royce Plc（罗尔斯·罗伊斯公司）
A Digital Twin (DT) is a simulation of a physical system that provides information to make decisions that add economic, social or commercial value. The behaviour of a physical system changes over time, a DT must therefore be continually updated with data from the physical systems to reflect its changing behaviour. For resource-constrained systems, updating a DT is non-trivial because of challenges such as on-board learning and the off-board data transfer. This paper presents a framework for updating data-driven DTs of resource-constrained systems geared towards system health monitoring. The pr…
Sparse Attention-driven Quality Prediction for Production Process Optimization in Digital Twins
School of Mechanical and Electrical Engineering, Kunming University of Science and Technology（昆明理工大学机电工程学院）；School of Electrical and Data Engineering, University of Technology Sydney (UTS)（悉尼科技大学电气与数据工程学院）；Yunnan Key Laboratory of Intelligent Control and Application, Kunming University of Science and Technology（昆明理工大学云南省智能控制与应用重点实验室）；ChingMu Tech. Ltd.（清沐科技有限公司）；National Research Foundation, Singapore（新加坡国家研究基金会）；Infocomm Media Development Authority（新加坡资讯通信媒体发展管理局）；Defence Science Organisation (DSO) National Laboratories（新加坡国防科学组织国家实验室）；Singapore Ministry of Education (MOE)（新加坡教育部）；NTU Centre for Computational Technologies in Finance (NTU-CCTF)（南洋理工大学金融计算技术中心）
