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Nowadays many processes in the food industry are monitored in an automatic way, with the purpose of minimising the need for workforce and of ensuring the proper control of the quality and safety of the foodstuff. All the sensors share data with a centralised management unit, where often a Manufacturing Execution System collects and evaluates them. As reported in recent research, however, a further step that can be undertaken, exploiting Industry 4.0 enabling technologies, is the implementation of digital twin approaches, with the additional aim to prevent possible issues during production. In line with these considerations, this work aims at showing two different digital twin models intended for improving the control of as many real food systems. Liquid and powder fluids are taken as examples for highlighting the differences in the optimization of the two food processes, as well as for fully exploring the potential of the digital twin approach. Finally, based on the real data taken from two pilot plants, a framework for the selection of the best digital twin tool in the food sector is delineated. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore adaptation-based and stability-based views of supply chain resilience to analyse what insights these different perspectives, individually and collectively, offer for theory and practice. In the stability-based view, resilience is triggered by disruptions and performance deviationsto return to some 'normal' states. This view accounts for known-known uncertainty. The adaptation-based view shifts the focus from avoiding oscillations and recovering some stable states toward proactive adaptation and performance persistence. The adaptation-based view aims at designing structurally adaptable networks with process flexibility and actively used redundancy. It considers resilience from the value-creation perspective accounting for unknown-unknown uncertainties. Stability-based approach views resilience as an outcome or quantity. Adaptation-based approach considers resilience as a property or quality. A combination of stability- and adaptationbased approaches is imperative for building a strong supply chain immunity through an integration of general protection and adaptability. These approaches complement each other depending on the knowledge of and attitude to uncertainty by decision-makers. A combination of the two views helps consider resilience both as a quantity to measure how sick the supply chain is and to understand how the resilience comes about to ensure the quality of the network health and viability. [ABSTRACT FROM AUTHOR]

Photogrammetry systems are widely used in industrial manufacturing applications as an assistance measurement tool. Not only does it provide high-precision feedback for assembly process inspection and product quality assessment, but also it can improve the flexibility and robustness of manufacturing systems and production lines. However, with growing global competition and demands, companies are forced to enhance production efficiency, shorten production lifecycle and increase product variety by incorporating reconfigurable factory design that can meet challenging timeline and requirements. Although dynamic facility layout is widely investigated, the position selection for the photogrammetry system in dynamic manufacturing environment is usually overlooked. In this paper, dynamic layout of the V-STARS photogrammetry system is investigated and optimised in a digital-twin environment using deep reinforcement learning. The learning objectives are derived from the field of view (FoV) evaluation from point clouds 3D reconstruction, and collision detection from the digital twin simulated in Visual Components. The application feasibility of the proposed dynamic layout optimisation of the V-STARS photogrammetry system is verified with a real world industrial application. [ABSTRACT FROM AUTHOR]

Digital Twins (DTs) hold significant promise in addressing the challenges faced by food supply chains (FSCs). This paper aims to provide critical insights into the potential for Digital Twins to meet the key challenges of the FSC and establish a comprehensive conceptual framework for their implementation. Following a systematic literature review (SLR), the study identified 81 peer-reviewed, high-quality papers published over the last decade (2012–2023). The typology-driven thematic analysis emphasises the emergent nature of DTs within FSCs, highlighting their key characteristics including monitoring, real-time simulation, and scenario analysis. The identified characteristics, applications, implementation drivers and barriers of Digital Twin form the basis for a novel conceptual framework for implementing DTs in FSCs. Leveraging insights from Innovation Adoption Theory and the Technology-Organization-Environment (TOE) framework, the study outlines a structured five step implementation process divided into three stages. Notably, technology assessment and performance evaluation emerge as two innovative steps necessary for the successful implementation of DTs specifically, not previously considered by the theory. The study identifies promising avenues for future research. These findings provide invaluable guidance to researchers and practitioners seeking to embrace the potential of Digital Twin within the food industry. [ABSTRACT FROM AUTHOR]

Rapid advances in new-generation information technologies have been the main driving force for the transformation of manufacturing enterprises in Industry 4.0. Digital twin (DT), as a key technology to promote intelligent manufacturing, has shown great potential for manufacturing enterprises to create an industrial intelligence-driven production equipment through in-depth integration of cyber-physical systems. However, the lack of a systematic effective DT modelling method with a supporting evaluation metric is the most important factor restricting the application of DT in manufacturing enterprises. To bridge the gap, this paper proposes a novel multi-level modelling and fidelity evaluation (MLM&FE) method of DT for creating smart production equipment in manufacturing enterprises, which could help enterprises establish an industrial intelligence-driven production environment to quickly respond to changes in the customised global market, thus greatly improving competitiveness of the enterprises. Specifically, this paper firstly designs a reference framework for DT-enhanced smart production equipment, on which an MLM&FE architecture is proposed. Then, key implementation methodologies and tools for MLM&FE are introduced from the perspective of data space modelling, virtual space modelling, knowledge space modelling, model integration and evaluation. Finally, the developed smart production equipment prototype demonstrates the feasibility and effectiveness of DT MLM&FE. [ABSTRACT FROM AUTHOR]

Production systems have to be adapted continuously to changing circumstances. This means that the responsible production planner has to frequently make decisions about reconfiguring complex systems under uncertainty whose outcome greatly affects the company's business success. Discrete-event simulation is one powerful tool to support the necessary analysis and scenario evaluation, but still remains time-consuming and tricky to set up and to maintain. When implemented as a digital twin of the production system, the simulation model can maintain a high degree of accuracy over a long time period. An approach to realise this potential is presented and illustrated in this paper with a use case from the automotive industry. This paper contributes several new methods and findings to the development of digital twins of production systems: Firstly, it demonstrates how exceptional events in the validation of the digital twin can be handled. Secondly, it shows how structural changes in the system can be discovered using data on machine activity and process mining. Thirdly, the paper introduces a possibility on how to assess the accuracy of the digital twin. Furthermore, it demonstrates how to assess the robustness of the digital twin to estimation errors in machine processing times. [ABSTRACT FROM AUTHOR]

The adoption of simulation models as Digital Twins (DTs) has been standing out in recent years and represents a revolution in decision-making. In this context, we note increasingly faster and more efficient decisions by mirroring the behaviour of physical systems. On the other hand, we highlight the challenges to ensure the simulation models validity over time since traditional validation approaches have limitations when we consider the periodic update of the model. Thus, the present work proposes an approach based on the constant assessment of these models through Machine Learning and control charts. To this end, we suggest a monitoring tool using the K-Nearest Neighbors (K-NN) classifier, combined with a p-control chart, to periodically assess the validity of DT simulation models. The proposed approach was tested in several theoretical cases and also implemented in a real case study. The findings suggest that the proposed tool can monitor the DT functioning and identify possible special causes that could compromise its results. Finally, we highlight the wide applicability of the proposed tool, which can be used in different DT models, including near/real-time models with different characteristics regarding connection, integration, and complexity. [ABSTRACT FROM AUTHOR]

Digital twins became of greater interest to researchers and practitioners in supply chain and operations management (SCOM). Literature has addressed the need to understand digital twins in SCOM, mostly focusing on fragmented technological solutions and use cases. We start with an integrative literature review to determine which elements belong to research on digital twins in SCOM. We define the seven major elements of a digital twin in SCOM: technology, people, management, organisation, scope, task, and modelling. We also distinguish five major types of digital twins in SCOM: product, process, organisation, supply chain and network-of-networks. Illustration of a SCOM digital twin is provided using an anyLogistix example. We conclude that digital twins in SCOM are not merely a simulation-based replica of a real object but a complex socio-technical phenomenon involved in continuous human-artificial intelligence interactions. This leads to an understanding of the role of digital twins through the lens of Industry 5.0, reconfigurable and viable supply chains. Researchers and practitioners alike can use our framework to structure the knowledge on SCOM digital twins and consider all seven elements when designing and using digital twins. [ABSTRACT FROM AUTHOR]

High quality, safe, and reliable batteries are essential for widespread adoption of electric vehicles. Current Li-ion battery pack manufacturing processes rely on manual inspections to ensure electric vehicle battery quality. Such manual quality control is prone to errors, increasing the chances of defective batteries. This is likely to increase safety and reliability concerns in the public imagination, slowing down the adoption of electric vehicles. Furthermore, manual inspection is time-consuming and likely to become a bottleneck in scaling up electric vehicle battery production. A potential solution to address this need for fast and accurate inspection of batteries is the use of machine vision and robotics. In this study, we use digital twin design and simulation to develop a battery module inspection system that uses cobots and machine vision to inspect electric vehicle batteries for defects. Our proposed system can automate visual quality checks that are currently being done by human operators. The proposed cobotic system has been simulated and validated for a variety of battery defects to achieve fast and reliable detection. Since a digital twin of the cobotic inspection workcell has been used, the battery inspection system, as designed and validated, is ready for immediate implementation. [ABSTRACT FROM AUTHOR]

As electric vehicle adoption accelerates and demand increases, the inability to produce batteries in sufficient quantities has emerged as a critical bottleneck in the electric vehicle supply chain. Given the impending climate change crisis, resolving this bottleneck is imperative to accelerate the transition to a zero-emission electric mobility future. One potential solution is the use of robotics for fast and cost-effective assembly of batteries at scale. This study proposes a three-stage digital twin design and analysis method to develop robotic workcells for fast and cost-effective assembly of electric vehicle battery modules. Using digital twin design and simulation, robotic assembly line configurations have been developed for battery module production at different scales. Digital twin analytics was used to evaluate and optimise the proposed robotic battery assembly system for speed and cost. Industrial automation experts were consulted to further improve robotic work cell layouts to minimise investment in robots. Because digital twins of robotic workcells have been used, the configurations of the battery assembly line, as designed and validated, are ready for immediate implementation. For practitioners, this study offers heuristic methods to determine the appropriate assembly line configuration, the required number of robots and humans, for a desired production volume. For researchers, this study outlines promising areas for future investigation. [ABSTRACT FROM AUTHOR]

In recent years, the emerging Digital Twin (DT) paradigm under Industry 4.0 has been attracting more attention from both practitioners and academia due to its dynamic capabilities. Most DT studies are theoretical and deal with hypothetical analysis, whereas fewer studies are available on real-life empirical cases. Due to dynamic problem-solving capabilities, DT technologies are widely used in performance improvement analysis in food processing companies (FPC) despite limited implication's for business strategies. Our study incorporates the DT technologies with an implementation case study on FPC and accomplishes the real-life problem of the food processing company (FPC). Moreover, the proposed DT research framework demonstrates the various DT implementation stages, such as strategic mapping and physical-virtual space replica, with rigorous analysis. The results show that DT enhances the existing system's machine availability, allocation efficiency, technical efficiency, worker efficiency, utilization rate, effectiveness, step ratio, and throughput rate. The proposed physical-virtual interface model is executed using AnyLogic software with JAVA-enabled programming. [ABSTRACT FROM AUTHOR]

Numerous studies have focused on digital twins (DTs) and their applications. However, a common method for evaluating the performance of DTs is lacking. Digital twins performance assessment mechanism is crucial when improving or monitoring any process or system in a business, such a strategy could serve as a guide to help researchers and practitioners create more effective digital twins. Guided by these challenges, we propose a structured method for measuring the performance and flexibility of digital twins, and our study provides a procedure for quantitative calculation of the performance of DTs based on four key performance indicators (KPIs). Moreover, we propose a new KPI, DTflex, for determining the flexibility of digital twins. We evaluated the performance of DTflex as a KPI to demonstrate its use as a simple tool for designers and practitioners to quickly compare different DT methodologies, which might provide valuable input for the iterative improvement of DTs to increase their efficiency in future applications. The knowledge provided along with the method proposed in this paper is expected to help researchers and practitioners working in different fields to compare DT methodologies and DT performance which will lead to higher efficiency DT solutions and increased re-usage of existing DTs. [ABSTRACT FROM AUTHOR]

The Industry 4.0 revolution is changing the manufacturing landscape. A broad set of new technologies emerged (including software and connected equipment) that digitise manufacturing systems. These technologies bring new vitality and opportunities to the manufacturing industry, but they also bring new challenges. This paper focuses on the impact of Industry 4.0 on production planning approaches and software. We first propose a digital twin framework that integrates production planning systems and frontier technologies. The frontier technologies that may impact production planning software are the internet of things, cloud manufacturing, blockchain, and big data analytics. Second, we provide a state-of-the-art on the application of each technology in the production planning, as well as a detailed analysis of the benefit and application status. Finally, this paper discusses the future research and application directions in the production planning. We conclude that Industry 4.0 will lead to the construction of data-driven models for production planning software. These tools will include models built accurately from data, account for uncertainty, and partially actuate the decision autonomously. [ABSTRACT FROM AUTHOR]

Data has become a high-value commodity in manufacturing. There is a growing realisation that the data-driven applications could become strong differentiators of manufacturing enterprises. To guide the developments in digitisation, a widely accepted framework is needed. In the absence of the universal framework, the components making a digital enterprise are captured in an example framework that is introduced in the paper. The adoption of new technology and software solutions has increased complexity of manufacturing systems. In addition, new product introductions have become more frequent and the demand more variable. A digital space enables optimisation and simulation of decisions before their realisation in the physical space. Predictive modelling with its time dimension is a valuable actor in the digital space. Three challenges of predictive modelling such as model complexity, model interpretability, and model reuse are identified in this paper. The coverage of each challenge in the literature is illustrated with the recently published papers. The main aspects of these challenges and the synthesis of the developments in digital manufacturing are articulated in the form of eight observations that could guide the future research. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the literature concerning the implementation of digital twins (DTs) for zero-defect manufacturing (ZDM) following a systematic method and, guided by a preliminary finding that a structured and standardised approach to the development of the DT applications is lacking, we provide a standardised design methodology to guide researchers and practitioners in their efforts to develop DTs regardless of the domain. After examination and interpretation of the literature, we also present the results of our state-of-the-art analysis, discuss the current state and limitations of research and practice, and provide useful insights on this important and complex topic. The design methodology proposed in our study will benefit both practitioners and academicians by covering the essential elements to be considered when developing DTs for ZDM for any applications in this domain. The study also contributes to knowledge by presenting a structured overview of the specific research area with a comprehensive, systematic, and critical analysis of the literature and by providing answers to some fundamental questions in the context of DTs for ZDM. Finally, we provide suggestions for further developments in research and practice. [ABSTRACT FROM AUTHOR]

Digital platforms are transforming almost every industry today and are expected to revolutionize future value creation. Digital platforms require collaboration, but this is challenging especially in the manufacturing industry where sensitive data need shared and high investments are required. Therefore, the purpose of this study is to enhance the understanding of how to successfully collaborate on digital platforms in the manufacturing industry by developing a contingency framework. The study is an explorative single case study of a digital platform. More specifically, the study examined the development process of a digital twin platform created by a large high-technological company and its collaborative actors. The results are based on 21 semi-structured interviews and reveals that actors on digital platforms can face five types of challenges that hinder a successful collaboration: disadvantages of dependency, uncertainty regarding data management, varying customer needs, insufficient work methods, and unsuitable payment models. The analysis also reveals four strategies that can be used to address the challenges: transparency strategy, incentive model strategy, servitization strategy, and control strategy. Moreover, these findings are summarized in a contingency framework that explains which types of challenges that can be addressed with which strategies based on the specific prerequisites of each collaboration. [ABSTRACT FROM AUTHOR]

The manufacturing industry is experiencing a service-oriented transformation in the digitalisation era. However, many small and middle enterprises (SMEs) still rely on traditional manufacturing patterns in which they can hardly servitise manufacturing resources due to the limited budget and poor digitalisation capability. To servitise manufacturing resources, this paper proposes unified five-layer blockchain-enabled secure digital twin platform architecture, followed by its core enabling components and technologies. Firstly, a service-oriented digital twinning model is developed to transform physical resources into digital services. Secondly, a rule-based off-chain matching mechanism is designed to bridge customers' orders with manufacturing services. Thirdly, service-oriented architecture (SOA) is adopted as the major methodology to design and develop the whole blockchain platform. Four blockchain frontend services are developed using React.js, whilst the blockchain backend is developed using private Ethereum blockchain and InterPlanetary File System (IPFS). Finally, an experimental case is conducted based on the 3D printing scenario to verify the effectiveness and efficiency of the proposed platform, named imseStudio. The results show that it not only provides an effective solution to digitalise manufacturing resources but also promotes the transformation towards service manufacturing. Highlights Blockchain-enabled secure digital twin platform is developed to servitise manufacturing resources Service-oriented digital twinning model is developed to transform physical resources into digital services Rule-based off-chain matching mechanism is built to bridge customers' orders with 3D printer Four blockchain explorers are developed to facilitate 3D printing services [ABSTRACT FROM AUTHOR]

Social manufacturing is conceptualised as a new type of networked manufacturing paradigm that supports the organisation of socialised manufacturing resources (SMRs) to satisfy the growth of personalised demands on crowd intelligence in a timely manner when co-creating open architecture products. The high participation of each stakeholder in this type of product production places a higher requirement on sufficient collaboration among social, cyber and physical spaces. However, under a decentralised social manufacturing network, the management of SMRs has encountered some real-life challenges in terms of their distributed and heterogeneous features. Hence, this paper proposes a blockchain-enabled digital twin collaboration platform (BcDTCP) as an integrated solution to address these challenges. A hybrid domain-driven design method is presented to design and implement business-knowledge driven systems. To address the heterogeneity of SMRs, a ubiquitous object structure is designed to flexibly adjust the functionality of the digital twin. Blockchain is introduced to construct a peer-to-peer network to organise the SMRs in a decentralised manner. Additionally, a timed coloured Petri net-based workflow is adopted to formulise the collaboration logic into a smart contract executed on the blockchain. Finally, a demonstrative case study is conducted to verify and evaluate the proposed BcDTCP under a 3D printing scenario. [ABSTRACT FROM AUTHOR]

AbstractIn the field of autonomous driving research, the use of immersive virtual reality (VR) techniques is widespread to enable a variety of studies under safe and controlled conditions. However, this methodology is only valid and consistent if the conduct of participants in the simulated setting mirrors their actions in an actual environment. In this paper, we present a first and innovative approach to evaluating what we term the behavioural gap, a concept that captures the disparity in a participant’s conduct when engaging in a VR experiment compared to an equivalent real-world situation. To this end, we developed a digital twin of a pre-existed crosswalk and carried out a field experiment (N = 18) to investigate pedestrian-autonomous vehicle interaction in both real and simulated driving conditions. In the experiment, the pedestrian attempts to cross the road in the presence of different driving styles and an external Human-Machine Interface (eHMI). By combining survey-based and behavioural analysis methodologies, we develop a quantitative approach to empirically assess the behavioural gap, as a mechanism to validate data obtained from real subjects interacting in a simulated VR-based environment. Results show that participants are more cautious and curious in VR, affecting their speed and decisions, and that VR interfaces significantly influence their actions. [ABSTRACT FROM AUTHOR]

Colleges and universities are large consumers of energy, with a huge potential for building energy efficiency, and need to reduce energy consumption to build a low-carbon, energy-saving campus. Predicting the energy consumption of campus buildings can help to accurately manage the electricity consumption of buildings and reduce the energy consumption of buildings. However, the electricity consumption of a building's operation is affected by many factors, and it is difficult to establish a model for analysis and prediction. Therefore, in this study, the training building of the BIM education center on campus was selected as the research object, and a digital twin O&M platform was established by integrating IoT, digital twin technology (DDT), smart meter monitoring devices, and indoor environment monitoring devices. The O&M management platform can monitor real-time changes in indoor power consumption data and environmental parameters, and organize data on multiple influencing factors and power consumption. Following training, validation, and testing, the machine learning models (back propagation neural network, support vector model, and multiple linear regression model) were assessed and compared for accuracy. Following the multiple linear regression and support vector models, the backpropagation neural network model exhibited the highest accuracy. Consistent with the actual power consumption detection results in the BIM education center, the backpropagation neural network model produced results. Consequently, the BP model created in this study demonstrated its dependability and ability to forecast campus building power usage, assisting the university in organizing its energy supply and creating a campus that prioritizes conservation. [ABSTRACT FROM AUTHOR]

In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously. [ABSTRACT FROM AUTHOR]

This review focuses on the definitions, modalities, applications, and performance of various aspects of digital twins (DTs) in the context of transmission and industrial machinery. In this regard, the context around Industry 4.0 and even aspirations for Industry 5.0 are discussed. The many definitions and interpretations of DTs in this domain are first summarized. Subsequently, their adoption and performance levels for rotating and industrial machineries for manufacturing and lifetime performance are observed, along with the type of validations that are available. A significant focus on integrating fundamental operations of the system and scenarios over the lifetime, with sensors and advanced machine or deep learning, along with other statistical or data-driven methods are highlighted. This review summarizes how individual aspects around DTs are extremely helpful for lifetime design, manufacturing, or decision making even when a DT can remain incomplete or limited. [ABSTRACT FROM AUTHOR]

A reduced-order model was generated from aerodynamic simulation data for the optimal design and operation management of an air-jet ejector to remove contaminated materials in manufacturing plants. Three significant design parameters of the nozzle structure after studying the computational fluid dynamics (CFD) of the preliminary design cases were determined. Polynomial type and deep neural feedforward network-based meta-models were established based on parametric CFD simulations considering three design variables, which were used to predict the suction performance; the models were successfully validated by comparing the results with those of the complete CFD. Finally, a proper orthogonal decomposition was adopted to reconstruct an internal flow field within a few seconds to facilitate real-time monitoring of the air flow without complete CFD simulations, which was realized as a digital twin for the air-jet ejector system. [ABSTRACT FROM AUTHOR]

Mathematical modeling of bearing faults, worn tool holder taper contact interface, and unbalance are presented and integrated into a digital dynamic model of spindles in Part I of this paper. These faults lead to changes in preload and dynamic stiffness over time, consequently resulting in observable vibrations. This paper predicts the vibrations of a spindle at a particular measurement location by simulating the presence of a specific fault or multiple faults during spindle rotation. The vibration spectra generated by the digital spindle model at the spindle speed and its harmonics, the changes in the natural frequencies, and dynamic stiffnesses are correlated to faults with experimental validations. The simulated vibration spectrums are later used in training an artificial neural network for fault condition monitoring presented in Part III of the paper. [ABSTRACT FROM AUTHOR]

Engineer-to-order manufacturing, characterized by highly customized products and complex workflows, presents unique challenges for warehouse management and operational efficiency. This paper explores the potential of a digital twin as a transformative solution for engineer-to-order environments in manufacturing companies realizing prefabricated building components. This paper outlines a methodology encompassing users' requirements and the design to support the development of a digital twin that integrates Internet of Things devices, Building Information Modeling, and artificial intelligence capabilities. It delves into the specific challenges of outdoor warehouse optimization and worker safety within the context of engineer-to-order manufacturing, and how the digital twin aims to address these issues through data collection, analysis, and visualization. The research is conducted through an in-depth analysis of the warehouse of Focchi S.p.A., a leading manufacturer of high-tech prefabricated building envelopes. Focchi's production processes and stakeholder interactions are investigated, and the paper identifies key user groups and their multiple requirements for warehouse improvement. It also examines the potential of the digital twin to streamline communication, improve decision-making, and enhance safety protocols. While preliminary testing results are not yet available, the paper concludes by underlining the significant opportunities offered by a BIM-, IoT-, and AI-powered digital twin for engineer-to-order manufacturers. This research, developed within the IRIS project, serves as a promising model for integrating digital technologies into complex warehouse environments, paving the way for increased efficiency, safety, and ultimately, a competitive edge in the market of manufacturing companies working in the construction industry. [ABSTRACT FROM AUTHOR]

In conventional engineering education, issues such as the discrepancy between virtual and real environments, rigid practical operations, lack of reflective support, and a disconnect between online and offline learning prevail. Digital twin technology, with its high fidelity and real-time interaction features, presents an innovative instructional aid for engineering education. This study developed a digital twin learning system to assist instructors in implementing project-based teaching models in landscaping technology courses. To assess the effectiveness of this system, a quasi-experiment was designed. Seventy students from a vocational high school majoring in landscaping technology in China were recruited as participants. These students were divided into two groups, each consisting of 35 students, with the same teaching pace. The experimental group utilized the system to supplement the instructor's teaching of landscaping courses, while the control group received instruction through traditional methods. The experiment lasted for eight weeks, comprising a total of 16 classes. Ultimately, the results indicated that students in the experimental group significantly outperformed those in the control group in critical thinking, cognitive load, learning experience, and academic performance. Additionally, this research examined the acceptance of learners toward using the digital twin learning system and its influencing factors based on the Technology Acceptance Model, aiming to provide insights into enhancing engineering education courses teaching effectiveness and targeted technological development. [ABSTRACT FROM AUTHOR]

The energy consumption during the operation and maintenance phase of buildings is huge. As the built-up area in China increases, the demand for energy conservation in existing buildings has become a key focus of its dual carbon policy. Intelligent operation and maintenance based on digital twins is an emerging means to reduce carbon emissions from buildings, but it faces some problems in the process of promotion. Complete digital and intelligent transformation requires significant investment and has certain requirements for project parties and operation and maintenance teams. Small businesses or individual households have relatively simple requirements for intelligent operation and maintenance scenarios and do not require complete digital twins. To address the above issues, this article uses an affordable universal digital twin framework to provide a digital solution for intelligent operation and maintenance of existing buildings. This solution allows networking communication between devices and uses IoT modules to monitor and control the environment. This digital twinning model can reduce the measurement and control of energy-consuming end devices without on-site transformation and has rich scalability. This article uses the solution to deploy an office at a university in Henan Province and specifically measures the power consumption of displays, indoor environment, and air conditioning. According to the needs, it expands the space occupation, fans, air handlers, lights, and other end devices of the digital twin. The digital twin accurately presents the energy consumption of the office during extreme weather conditions, which has an auxiliary role in promoting digital twins in the region and optimizing energy consumption in existing buildings. [ABSTRACT FROM AUTHOR]

Remaining useful fatigue life monitoring of wind turbine drivetrains is important. However, the implementation of real‐time monitoring often faces efficiency and accuracy challenges. In order to resolve this, this paper proposes a vibration‐based damage monitoring digital twin (VBDM‐DT) that enables the online intelligent evaluation of wind turbine gearboxes, using gear tooth surface durability as an example fatigue mode. The VBDM‐DT integrates a random wind load model, a high‐fidelity dynamics model, and a fatigue damage model. The random wind load model takes the wind speed from the supervisory control and data acquisition (SCADA) as input to estimate the input torque of the drivetrain in real time. Simultaneously, VBDM‐DT uses the vibration signals from the condition monitoring system (CMS) to intelligently calibrate the dynamics model, allowing it to be continuously adjusted and optimized in response to actual vibrations. The fatigue damage model takes the real‐time dynamic loads estimated by the high‐fidelity dynamic model as input to achieve real‐time fatigue damage monitoring of key components in the wind turbine gearbox. Applying the VBDM‐DT model to a 2 MW wind turbine gearbox, the results indicate that the model provides satisfactory accuracy in estimating input loads and good adaptability in intelligent calibration of the dynamic model. Based on this model, the fatigue life estimation for gears and bearings is more credible and reliable. [ABSTRACT FROM AUTHOR]

With the development of simulation technology, more and more manufacturers have begun to use the digital twin to design workshops and factories. For these design scenarios under real-time interaction requirements with an excessive amount of model data, if the rendering is stuck, it will reduce the work efficiency. It is a key enabling technology to simplify and switch the geometry models with different resolutions, according to the distance of the viewpoint or the motion state to reduce the computational complexity. Existing model simplification methods emphasize the universality and efficiency under various scenarios, while the simplification performance in the 3D geometry models of industrial mechanisms is poor. This paper proposes a rapid simplification approach to the 3D geometry model of mechanisms in the digital twins-driven manufacturing system design context. A novel Vertex Saliency-oriented Classified Edge Collapse (VS-CEC) algorithm is proposed to simplify the shape feature of the 3D geometry model of mechanisms. It especially emphasizes solving the sharp shape preservation issues in the mechanical design scenario rather than a universal things design scenario. A vertex saliency factor is defined and integrated with the region boundary information obtained from the processing of detailed features to ensure visual fidelity as well as shape preservation such as sharp edges. Experiments show that this approach reduces the data model complexity more reasonably to speed up the rendering. It ensures that the digital twin model interacts quickly with the physical manufacturing system, and thus realizes the low-latency visual effect of cyber-physical synchronization. [ABSTRACT FROM AUTHOR]

Autoclave is the most important equipment in the composite curing process, and its real-time condition has a direct impact on the quality of composite materials. Therefore, rapid and precise fault diagnosis reasoning is of great significance for the autoclave. To address the shortage of signed directed graph (SDG)-based fault diagnosis method, this paper proposes a fault diagnosis method based on digital twin (DT) enhanced SDG for autoclave. Firstly, the SDG model of autoclave temperature control system is constructed, and the model is improved and enhanced by pre-fault transition state identification, fuzzy confirmation of node states, and simplification of potential branch circuits by using DT. The effectiveness of the method in this paper is verified by fault diagnosis based on SDG and DT-SDG methods respectively. The experimental results show that the method proposed in this paper can improve the speed and resolution of fault diagnosis by reducing the number of potential fault propagation paths and the number of inferences. [ABSTRACT FROM AUTHOR]

The development of advanced information technologies are paving the digital transformation of manufacturing systems, of which Digital Twin-based manufacturing system (DTMS) has become a prevailing topic attracted ever-increasing concerns from both industry and academia. As a cutting-edge smart manufacturing system, DTMS can improve manufacturing accuracy and efficiency based on high fidelity simulation, near real-time monitoring and control in a cyber-physical integrated manner. However, the connotation and boundary of DTMS lack a clear definition and systematic analysis. Therefore, this paper reviews the existing Digital Twin reference models and implementation architectures on manufacturing system, and further proposes a reference model of DTMS. Based on it, the characteristics and operational mechanism of DTMS are analyzed from three perspectives: hierarchy, dimension, and scale. Specifically, the composition of DTMS is described from a multi-level perspective, the specific characteristics of the DTMS are analyzed from a multi-dimensional perspective, and the temporal and spatial characteristics of the DTMS under different application scenarios are depicted from a multi-scale perspective, respectively. At last, the potential research directions of DTMS are highlighted in terms of reusability, interpretability and adaptability. It is envisioned that this work can provide a clear understanding with insightful knowledge to attract more in-depth research of DTMS. [ABSTRACT FROM AUTHOR]

Risks in the supply chain can damage many companies and organizations due to sustainability risk factors. This study evaluates the supply chain risk assessment and management and then selects the best supplier in a gas company in Egypt. A comprehensive methodology can use the experts' opinions who use the linguistic variables in the spherical fuzzy numbers (SFNs) to evaluate the criteria and suppliers in this study based on their views. Selecting the best supplier is a complex task due to various criteria related to supply chain risk assessment, such as supply risks, environmental risks, financial risks, regularity risks, political risk, ethical risks, and technology risks and their sub-criteria. This study suggested a new combined model with multi-criteria decision-making (MCDM) under a spherical fuzzy set (SFS) environment to overcome uncertainty and incomplete data in the assessment process. The MCDM methodology has two methods: the Entropy and COmbinative Distance-based Assessment (CODAS) methods. The SFS-Entropy is used to compute supply chain risk assessment and management criteria weights. The SFS-CODAS method is used to rank the supplier. The main results show that supply risks have the highest importance, followed by financial and environmental risks, and ethical risks have the lowest risk importance. The criteria weights were changed under sensitivity analysis to show the stability and validation of the results obtained from the suggested methodology. The comparative analysis is implemented with other MCDM methods named TOPSIS, VIKOR, MARCOS, COPRAS, WASPAS, and MULTIMOORA methods under the SFS environment. This study can help managers and organizations select the best supplier with the lowest sustainability risks. [ABSTRACT FROM AUTHOR]

AbstractThis paper presents a method to create a high-fidelity gearbox thermal model. A hybrid analytical-empirical model is presented, it is based on the thermal network approach and experiments are used to determine some unknown parameters of the model. A test campaign was led on a four stages helical gear unit and consisted of cooling measurements, no-load tests and loaded tests. These experiments were performed under different rotational speeds and loads. The results show that the developed thermal model provides an accurate prediction of the thermal behavior of different components of the gearbox (namely rolling element bearings and oil) by only using few thermal sensors. These results are corroborated by power losses measurements which demonstrate that the model can estimate the gear unit efficiency. [ABSTRACT FROM AUTHOR]

The European Green Deal (EGD) represents the response of the European Union (EU) to the climate crisis and a strategy to mitigate against climate change and the associated environmental degradation. European Climate Law supports the EGD by setting the legally binding target of climate neutrality for the EU on a time horizon of 2050 and mandates that member states develop and implement their own Climate Action Plans (CAP), which are strategic roadmaps outlining steps to reduce greenhouse gas (GHG) emissions and adapt to the impacts of climate change. Within the Irish context, the CAP focuses on six specific sectors, three of which, namely the energy grid, the built environment and transportation are considered in this research. This policy proposes the retrofitting of all residential buildings in the country to a B2 energy performance certificate (EPC), all public transport and 60% of private vehicles electrified and the full decarbonisation of the electrical grid by 2050. This study aims to implement the targets and goals outlined within CAP 2021 in an urban area of Ireland, specifically a historical city centre. The research leverages digital twin technology developed by IES to model the carbon emissions associated with the built environment, while also incorporating GHG emissions associated with transportation and the grid. Furthermore, CAP scenarios are applied systematically and strategically, prioritising building retrofits based on buildings with similar characteristics, typologies and performance. This study has assessed the performance of CAP about two future grid scenarios (1) business as usual and (2) a decarbonised grid. In both scenarios, CAP was not sufficient to achieve the targets set out, resulting in a shortfall of between 25 and 33%. Based on this outcome it may be concluded that any decarbonisation targets specified should be tested and validated before their implementation in policy. Moreover, current state-of-theart digital twin technologies can facilitate the rapid holistic appraisal of complex multi-sectoral policy at the urban scale, which, depending on data availability, could prove useful in future iterations of CAP at both national and international levels. [ABSTRACT FROM AUTHOR]

DT systems, characterized by real-time capabilities, high precision, and high integration, have become essential in various domains. In the context of cultural heritage, a DT system encompasses comprehensive information about heritage sites, contextual data, and expert knowledge, forming a complex dynamic system. The substantial volume of information and diverse sources significantly increases the cognitive load for management personnel in understanding on-site situations. This study, from the perspective of situational awareness, introduces X-reality technologies (VR and AR) into DT systems for cultural heritage risk management. The aim is to evaluate the effectiveness of different X-reality technologies in cultural heritage risk perception and their impact mechanisms. A total of 184 participants were divided into two groups and experienced three different applications (2D desktop, VR, and AR). Using situational awareness rating techniques, participants' responses were measured across three dimensions: attention resource demand, attention resource supply, and understanding of the situation. SEM was employed to estimate the stability of the scale data. The results indicate that, compared to traditional 2D desktops, both VR and AR demonstrate advantages in enhancing heritage risk situational awareness. However, in AR mode, no significant advantages were found in the dimensions of attention resource demand and attention resource supply compared to traditional 2D desktop applications. Furthermore, a significant difference in immersion between VR and AR was found to affect the attention resource demand dimension. Although the results suggest differences in the interactivity of VR and AR in affecting the attention resource demand dimension, no significant differences were found. By comprehensively understanding the functional mechanisms of X-reality technologies in influencing cultural heritage risk situational awareness, this study provides design references for constructing DT systems for cultural heritage risk management. Additionally, it offers insights for heritage site managers, experts, and stakeholders to enhance risk perception efficiency, promoting more effective risk assessment, analysis, and strategic decision-making, thereby reducing damage to cultural heritage. [ABSTRACT FROM AUTHOR]

Construction of wireless infrastructure using unmanned aerial vehicle (UAV) can effectively expand the coverage and support high-density traffic of next-generation communication systems. Designing wireless systems including UAVs as aerial base stations (ABSs) is a challenging task, due to the mobility of ABSs causing time-varying nature of environmental surroundings and relative propagation paths to user equipment (UE) devices. Therefore, it is essential to have an accurate estimate of the channel for varying positioning of the UAVs. In this paper, we propose to adopt a digital twin based performance evaluation procedure for wireless systems including ABSs, providing enhanced accuracy of channel modeling for specific target deployment areas. Using ray-tracing channel models reflecting detailed building and terrain information of the transmission environment, an UAV position optimization algorithm based on reinforcement learning is presented. By utilizing deep deterministic policy gradient (DDPG), the proposed algorithm calculates the overall throughput in the digital twin and determines the desired states of the UAV. Performance evaluation results demonstrate the trajectory training ability of the algorithm and the performance advantage of the system with a reduced amount of shadow area compared to those with ground base stations (GBSs). [ABSTRACT FROM AUTHOR]

The pre-cooling of a large LNG storage tank involves complex phenomena such as heat transfer, low-temperature flow, gas displacement, and vaporization. The whole pre-cooling process could take up to 50 h. For large-scale, full-capacity storage tanks, it is particularly important to accurately control the pre-cooling temperature. Digital twin technology can characterize and predict the full life cycle parameters from the beginning of pre-cooling development to the end and even the appearance of damage in real time. The construction of a digital twin platform requires a large number of data samples in order to predict the operating state of the device. Therefore, a simulation method with high computational efficiency for the pre-cooling process of LNG tanks is of great importance. In this paper, the mixture model and discrete phase model (DPM) are applied to simulate the pre-cooling process of a large LNG full-capacity tank. Following Euler–Lagrange, the DPM greatly simplifies the solution process. Compared with the experimental results, the maximum error of the DPM simulation results is less than 11%. Such a highly efficient simulation method for the large LNG full-capacity storage tank can make it possible to build the digital twin platform that needs hundreds of data model samples. [ABSTRACT FROM AUTHOR]

A healthcare-focused version of the Internet of Things (IoT), the Internet of Medical Things (IoMT) enables real-time monitoring and remote medical support via integrated medical devices, programs, and support solutions. However, patients' safety and anonymity are in jeopardy by the open access networks employed in IoMT, which makes the systems susceptible to several threats and security lapses. By harnessing the synergies of blockchain, cloud computing, and digital twins, this study presents a comprehensive architecture and a secure lightweight authentication mechanism (which integrates the benefits offered by Yu and Park, Yu et al., and Amintoshi et al.) that addresses these concerns. The suggested method entails using session keys for secure communication while authenticating medical professionals and patients through a gateway. Cloud computing offers a flexible and robust framework for managing and storing medical data. Additionally, it simulates digital twins to enable data-driven decision-making and predictive analysis, and the incorporation of blockchain offers a decentralized and immutable ledger for recording and validating patient data and transaction logs enhancing data integrity, transparency, and traceability. Healthcare systems may confidently embrace the potential of IoMT by implementing this framework since it offers promising solutions to enhance the security and confidentiality of patient data in IoMT while supporting the provision of the best healthcare services, especially in emergency scenarios like the COVID-19 pandemic. The suggested approach is subjected to a thorough security evaluation using AVISPA, demonstrating its resistance to various attacks. A comparative analysis has also been carried out to assess the performance and computational cost of IoMT-BADT in comparison with other authentication schemes. [ABSTRACT FROM AUTHOR]

To address the issues of low visibility, poor real-time performance, and weak interaction capabilities in the operation of magnetic levitation circular knitting needles, an online monitoring method based on digital twin technology for magnetic levitation knitting needles is proposed. This method first conducts an in-depth analysis of the digital twin five-dimensional model of the magnetic levitation knitting needle to monitor the operational status of the equipment in real time. Additionally, an online monitoring system based on the Unity3D engine and a digital twin-based architecture for magnetic levitation knitting needles are designed. On this basis, the comprehensive monitoring, operation, and maintenance of the magnetic levitation knitting needles are achieved through the design of virtual models, visual interfaces, and real-time data acquisition and driving technology. Finally, this method takes a 5-inch 32-needle magnetic levitation circular knitting machine as the application research object. By analyzing the movement trajectory and real-time current feedback data of the three working positions of the magnetic levitation knitting needles, the rationality, effectiveness, and accuracy of the proposed method and model are demonstrated, achieving online monitoring of the magnetic levitation knitting needles and reflecting the full lifecycle process of the magnetic levitation knitting needles. [ABSTRACT FROM AUTHOR]

With the rapid development of science and technology, the integration and complexity of aerospace vehicles, weaponry, and large-scale chemical equipment are becoming higher and higher. PHM plays an important role in realizing reductions in equipment loss due to failures in many fields. In order to systematically sort through the research history of PHM and deeply analyze the development status of AR and DT technologies in the field of PHM, to clarify the current technical challenges and future development directions and to provide valuable references and insights for researchers, engineers, and decision-makers in the related fields, this paper summarizes the development of PHM in the engineering field. This paper summarizes the development of PHM in the field of engineering, from the initial PHM used in aerospace to the current PHM systems supported by various advanced technologies; analyzes the advantages and shortcomings of the digital twin and augmented reality technologies used for PHM; and organizes and summarizes the future directions and future research focuses of PHM research based on the existing technologies (mainly digital twins and augmented reality). After systematic research and study, we found that the integration of augmented reality and digital twin technologies will provide superb simulation capabilities and immersive operation and bring new challenges and opportunities. Therefore, it is also imperative to address the challenges and limitations that hinder the seamless integration of the new technologies. [ABSTRACT FROM AUTHOR]

Fault diagnosis of wind turbines has always been a challenging problem due to their complexity and harsh working conditions. Although data-mining-based fault diagnosis methods can accurately and efficiently diagnose potential faults, the visibility is extremely poor. In this paper, digital twin technology is introduced into the fault diagnosis of wind turbine drive train systems, and a wind turbine drive train fault diagnosis method based on digital twin technology is proposed, which monitors and simulates the actual operating condition in real-time by establishing a digital twin model of the wind turbine drive train. In addition, an improved variational modal decomposition combined with particle swarm optimization least squares support vector machine (IVMD-PSO-LSSVM) fault diagnosis method is proposed, which not only improves the accuracy of fault diagnosis but also effectively shortens the diagnosis time and strengthens the response speed of the system. Finally, a digital twin system for condition monitoring and fault diagnosis of wind turbine drive trains is developed based on the Unity 3D platform. Experiments show that the proposed IVMD-PSO-LSSVM can accurately identify fault types with an accuracy rate of 99.1%, which is an improvement of 3.4% compared to before. The proposed digital twin model can be used for real-time monitoring of wind turbine vibration data and provide a more intuitive real-time simulation of the wind turbine's operating status. This facilitates quick fault location and enables more accurate and efficient maintenance. [ABSTRACT FROM AUTHOR]

Cell level functions underlie tissue and organ physiology. Gene expression patterns offer extensive views of the pathways and processes within and between cells. Single cell transcriptomics provides detailed information on gene expression within cells, cell types, subtypes and their relative proportions in organs. Functional pathways can be scalably connected to physiological functions at the cell and organ levels. Integrating experimentally obtained gene expression patterns with prior knowledge of pathway interactions enables identification of networks underlying whole cell functions such as growth, contractility, and secretion. These pathways can be computationally modeled using differential equations to simulate cell and organ physiological dynamics regulated by gene expression changes. Such computational systems can be thought of as parts of digital twins of organs. Digital twins, at the core, need computational models that represent in detail and simulate how dynamics of pathways and networks give rise to whole cell level physiological functions. Integration of transcriptomic responses and numerical simulations could simulate and predict whole cell functional outputs from transcriptomic data. We developed a computational pipeline that integrates gene expression timelines and systems of coupled differential equations to generate cell-type selective dynamical models. We tested our integrative algorithm on the eicosanoid biosynthesis network in macrophages. Converting transcriptomic changes to a dynamical model allowed us to predict dynamics of prostaglandin and thromboxane synthesis and secretion by macrophages that matched published lipidomics data obtained in the same experiments. Integration of cell-level system biology simulations with genomic and clinical data using a knowledge graph framework will allow us to create explicit predictive models that mechanistically link genomic determinants to organ function. Such integration requires a multi-domain ontological framework to connect genomic determinants to gene expression and cell pathways and functions to organ level phenotypes in healthy and diseased states. These integrated scalable models of tissues and organs as accurate digital twins predict health and disease states for precision medicine. [ABSTRACT FROM AUTHOR]

As energy demand continues to grow, it is crucial to integrate advanced technologies into power grids for better reliability and efficiency. Digital Twin (DT) technology plays a key role in this by using data to monitor and predict real-time operations, significantly enhancing system efficiency. However, as the power grid expands and digitization accelerates, the data generated by the grid and the DT system grows exponentially. Effectively handling this massive data is crucial for leveraging DT technology. Traditional local computing faces challenges such as limited hardware resources and slow processing speeds. A viable solution is to offload tasks to the cloud, utilizing its powerful computational capabilities to support the stable operation of the power grid. To address the need, we propose GD-DRL, a task scheduling method based on Deep Reinforcement Learning (DRL). GD-DRL considers the characteristics of computational tasks from the power grid and DT system and uses a DRL agent to schedule tasks in real-time across different computing nodes, optimizing for processing time and cost. We evaluate our method against several established real-time scheduling techniques, including Deep Q-Network (DQN). Our experimental results show that the GD-DRL method outperforms existing strategies by reducing response time, lowering costs, and increasing success rates. [ABSTRACT FROM AUTHOR]

A virtual debugging and monitoring platform for industrial robots was designed based on digital twin technology to address the high cost and difficulty in maintaining and upgrading the industrial robot system platform in industrial robot experimental teaching. A digital twin model of a real industrial robot system platform was constructed using NX MCD software. Using PLCSIM Advanced high-performance simulator, virtual joint debugging of the entire industrial robot system platform achieved. With the help of OPC communication technology, the virtual platform established data interaction with the real PLC, achieving 3D visual monitoring of the real platform. The effectiveness of using digital twin technology to build a virtual platform was verified by comparing the synchronization accuracy with real robots. The application of this virtual platform effectively saves the on-site debugging cost of equipment, improves the efficiency, autonomy, and safety of experimental teaching, and enhances the visualization, intelligence, and digitalization level of experimental management. [ABSTRACT FROM AUTHOR]

Smart city sustainability initiatives prioritize creating environmentally, economically, and socially sustainable urban environments. Digital Twin (DT) technology creates precise digital replicas of physical assets, systems, or processes. These digital twins play a crucial role in advancing the goals of smart city sustainability. This paper explores the development and application of DT technology for integrated regional energy systems in smart cities, emphasizing its potential to optimize energy consumption, reduce costs, and enhance overall system performance. The CloudIEPS platform, an energy internet planning platform based on digital twin technology, is a great example of how digital twin technology can be applied in practice, helping optimize energy efficiency and reduce costs. Integrating digital twin technology with the Multi-Criteria Decision-Making (MCDM) methods offers a novel approach to managing and optimizing energy systems in smart cities. The paper aims to create a consistent and robust approach to determining the best digital twin solution for energy systems in smart cities. The paper identifies critical factors for decision-making and establishes a method for assessing the significance of criteria using Triangular Neutrosophic Sets (TNS) through the MEthod based on Removal Effects of Criteria (MEREC) and the Multi-Attributive Ideal Real Comparative Analysis (MAIRCA) approach. These methods are used to evaluate and prioritize multiple criteria in decision-making processes. Furthermore, the methods are combined with Triangular Neutrosophic Sets (TNS) to support decision-making for smart cities' energy systems, better accounting for the complex and uncertain nature of energy systems. A case study is conducted to apply and validate the developed methodology and perform a sensitivity analysis of the experimental results. The research outcomes indicated that the proposed methodology is robust and effective in handling the uncertainty and complexity inherent in smart cities' energy systems. The sensitivity analysis further confirms the stability and adaptability of the proposed methodology across different scenarios, making it a valuable tool for policymakers and stakeholders in the energy sector. [ABSTRACT FROM AUTHOR]

Historic Building Information Modelling (HBIM) plays a pivotal role in heritage conservation endeavours, offering a robust framework for digitally documenting existing structures and supporting conservation practices. However, HBIM's efficacy hinges upon the implementation of case-specific approaches to address the requirements and resources of each individual asset and context. This paper defines a flexible and generalisable workflow that encompasses various aspects (i.e., documentation, surveying, vulnerability assessment) to support risk-informed decision making in heritage management tailored to the peculiar conservation needs of the structure. This methodology includes an initial investigation covering historical data collection, metric and condition surveys and non-destructive testing. The second stage includes Finite Element Method (FEM) modelling and structural analysis. All data generated and processed are managed in a multi-purpose HBIM model. The methodology is tested on a relevant case study, namely, the church of Santa Ana in Seville, chosen for its historical significance, intricacy and susceptibility to seismic action. The defined level of detail of the HBIM model is sufficient to inform the structural analysis, being balanced by a more accurate representation of the alterations, through linked orthophotos and a comprehensive list of alphanumerical parameters. This ensures an adequate level of information, optimising the trade-off between model complexity, investigation time requirements, computational burden and reliability in the decision-making process. Field testing and FEM analysis provide valuable insight into the main sources of vulnerability in the building, including the connection between the tower and nave and the slenderness of the columns. [ABSTRACT FROM AUTHOR]

As digitalization is permeating all sectors of society toward the concept of "smart everything," and virtual technologies and data are gaining a dominant place in the engineering and control of intelligent systems, the Digital Twin (DT) concept has surfaced as one of the top technologies to adopt. This paper discusses the DT concept from the viewpoint of Modeling and Simulation (M&S) experts. It both provides literature review elements and adopts a commentary-driven approach. We first examine the DT from a historical perspective, tracing the historical development of M&S from its roots in computational experiments to its applications in various fields and the birth of DT-related and allied concepts. We then approach DTs as an evolution of M&S, acknowledging the overlap in these different concepts. We also look at the M&S workflow and its evolution toward a DT workflow from a software engineering perspective, highlighting significant changes. Finally, we look at new challenges and requirements DTs entail, potentially leading to a revolutionary shift in M&S practices. In this way, we hope to foster the discussion on DTs and provide the M&S expert with innovative perspectives. [ABSTRACT FROM AUTHOR]

Gear fault detection and remaining useful life estimation are important tasks for monitoring the health of rotating machinery. In this study, a new benchmark for endurance gear vibration signals is presented and made publicly available. The new dataset was used in the HUMS 2023 conference data challenge to test anomaly detection algorithms. A survey of the suggested techniques is provided, demonstrating that traditional signal processing techniques interestingly outperform deep learning algorithms in this case. Of the 11 participating groups, only those that used traditional approaches achieved good results on most of the channels. Additionally, we introduce a signal processing anomaly detection algorithm and meticulously compare it to a standard deep learning anomaly detection algorithm using data from the HUMS 2023 challenge and simulated signals. The signal processing algorithm surpasses the deep learning algorithm on all tested channels and also on simulated data where there is an abundance of training data. Finally, we present a new digital twin that enables the estimation of the remaining useful life of the tested gear from the HUMS 2023 challenge. [ABSTRACT FROM AUTHOR]

In this study, a digital twin model of a hydroelectric power plant has been created. Models of the entire power plant have been created and malfunction situations of a sensor located after the inlet valve of the plant have been analyzed using a programmable logic controller (PLC). As a feature of the digital twin (DT), the error prediction and prevention function has been studied specifically for the pressure sensor. The accuracy and reliability of the data obtained from the sensor are compared with the data obtained from the DT model. The comparison results are evaluated and erroneous data are identified. In this way, it is determined whether the malfunction occurring in the system is a real malfunction or a malfunction caused by measurement or connection errors. In the case of sensor failure or measurement-related malfunction, this situation is determined through the digital twin-based control mechanism. In the case of actual failure, the system is stopped, but in the case of measurement or connection errors, since the data are calculated by the DT model, the value in the specified region is known and thus there is no need to stop the system. This prevents production loss in the hydroelectric power plant by ensuring the continuity of the system in case of errors. [ABSTRACT FROM AUTHOR]

To build a digital twin model of intelligent longwall coal mining, this paper presents a new full-parameter position and attitude digital twin model of hydraulic supports. A full-parameter matrix describing the position and attitude of a support is established first. Then, the position and attitude digital twin model of a support based on this matrix is built. Afterward, a position and attitude digital twin model of the hydraulic support system in the workface is established using the Unity3D software and the Runge–Kutta algorithm as a motion calculation method. Finally, a simulation experiment is conducted based on the digital twin model of hydraulic supports, and results show its effectiveness. This digital twin model has the potential to control the hydraulic support system in future intelligent coal mining. [ABSTRACT FROM AUTHOR]