Academic literature on the topic 'Digital twin'

The creation of digital twins has been on a rise in the last years, and digital twins are becoming an essential part in today's industries. To be able to keep up, the engineering students at Högskolan i Halmstad need to follow and explore the current development. The goal with this project is to introduce digital twins to the students and give them a wider knowledge about the future of manufacturing.In this project a digital twin was developed from a physical crane. The digital twin was made from a CAD model and was imported into a simulator to replicate the physical robot and its parts. The digital twin had virtual sensors installed and may be considered a cyber-physical system.<br>På senare år har skapandet och användandet av digitala tvillingar ökat drastiskt och de håller på att implementeras i dagens industrier. För att ingenjör studenterna på Högskolan i Halmstad skall hänga med i utvecklingen av dagens tekniska samhälle, behöver de vara med och utforska de tekniska nyheter som finns. Målet med detta projekt är att introducera digitala tvillingar till ingenjör studenterna, för att ge dem en bredare grundkunskap inom framtidens industrier.I detta projekt har en digital tvilling utvecklats utifrån att efterlikna en fysisk kran. Utvecklingen av den digitala tvillingen har designats som en CAD-modell och importerats in i en simulator för att efterlikna den fysiska kranen och dess delar. På den digitala tvillingen har även virtuella sensorer installerats och systemet kan anses vara ett cyber-physical system.

Abstract. Digital Twin is a digital replica of physical assets, processes and systems that can be used for various purposes. Virtual model is constructed from the corresponding physical model and these two are then connected by generating real time data using sensors. Resent advances in technology have enabled this, because they have made digital environments cost-efficient. The goal is to simulate the real world with minimal physical resources using machine learning, multivariate analysis and other mathematical techniques. Optimally no physical resources go to waste in testing and development. Runtime validation means monitoring and validating a running system with both digital and physical models. Simulated (digital) model parameters are used in the physical model and then the simulated model is updated with new data from the physical model. Simulated model gains more and more data over time becoming more accurate. This thesis studies the applicability of mathematical models as a prediction tool to predict and validate systems behaviour as a part of simulation. And further, be used in analysis in a digital twin model.Tiivistelmä. Digitaalinen kaksonen on digitaalinen kopio fyysisistä voimavaroista, prosesseista ja systeemeistä, jota voidaan käyttää moniin tarkoituksiin. Virtuaalinen malli rakennetaan vastaavasta fyysisestä mallista ja nämä yhdistetään toisiinsa luomalla reaaliaikaista dataa sensoreilla. Viimeaikaiset kehitykset teknologiassa ovat mahdollistaneet tämän tekemällä digitaalisista ympäristöistä kustannustehokkaita. Tehtävänä on simuloida oikeaa maailmaa minimaalisilla fyysisillä resursseilla käyttäen koneoppimista, monimuuttuja-analyysiä ja muita matemaattisia tekniikoita. Parhaimmillaan fyysisiä resursseja ei menisi hukkaan testauksessa ja kehityksessä. Ajonaikainen validointi tarkoittaa toimivan systeemin tarkkailua ja validointia fyysisen ja digitaalisen mallin avulla. Simuloidun (digitaalisen) mallin arvoja käytetään fyysisessä mallissa ja simuloitua mallia päivitetään uudella datalla fyysisen mallin tulosten perusteella. Simuloitu malli saa yhä enemmän dataa ajan saatossa ja tätä myötä paranee. Tämä tutkielma tutkii matemaattisten mallien käytettävyyttä ennustukseen ja systeemin validointiin sekä simulaatiossa että digitaalisen kaksosen analyysissä.

Lo scopo di questa tesi è di analizzare il contesto creatosi nell’ultimo decennio attorno ai Digital Twin. La nascita di tecnologie abilitanti ha permesso ai gemelli digitali di essere riconosciuti come tecnologia di riferimento per guidare il percorso evolutivo nei domini applicativi in cui vi è un forte accoppiamento tra il mondo fisico e quello virtuale. Tra di essi troviamo: manifattura, sanità e città intelligenti. Inizialmente questo testo si concentrerà sull’individuare una definizione di riferimento che faccia chiarezza sui requisiti necessari per non fallire nell’adozione dei Digital Twin. Proseguendo poi nell’analizzare quali siano le tecnologie abilitanti che permettano ai Digital Twin di garantire i risultati attesi. Allo stesso tempo l’obiettivo è anche considerare quali difficoltà potrebbero creare insidie durante il percorso di adeguamento. L’elaborato si concentrerà poi sull’identificare quali siano i requisiti per una piattaforma di supporto a un’ ecosistema di Digital Twin. Sottolineando la differenza tra requisiti basilari: necessari all’abilitazione di una infrastruttura per tale scopo; e requisiti avanzati: che mirino a un ambiente evoluto che ospiti gemelli digitali intelligenti, capaci di imparare per poi autonomamente correggersi. Verranno poi trasportati questi requisiti sulle principali piattaforme di Cloud Computing con moduli di supporto ai Digital Twin. Permettendo un confronto che rilevi le caratteristiche comuni tra le piattaforme e allo stesso tempo mostri, per ognuna di esse, i rispettivi punti di forza.

This thesis evaluates the significance of the Digital Twin based data-driven solution, in helping corrective maintenance technicians leverage their multi-disciplinary engineering skills to solve complex mechatronic problems. Due to the complex mechatronic nature of the faults, human involvement is necessary for corrective maintenance. Even today, many industries perform corrective maintenance by following methods that are both time inefficient and error prone. Software/AI based solutions have been widely reported to have failed due to neglect of human aspect in maintenance. The role of human cannot be completely replaced by software systems yet. Standard maintenance practices such as FMEA and RCA are costly, time consuming and susceptible to errors. On the other side, Digital Twin (DT) based solutions have shown to have improved management and effectiveness of maintenance by considering the human aspect. However, for corrective maintenance, the solution is still in its conceptual stage. There is a need to practically implement a Digital Twin based solution and quantitatively evaluate its significance. Recent studies have shown that Digital Twin concept, built on model-based approach, has a tremendous potential in providing all the essential data required to control the behaviour of a network of physical devices, and at the same time, virtually monitor their real-world states effectively. This thesis first attempts to develop user-centric visualizations built on a fully integrated digital twin of a complex Cyber Physical Production System (CPPS), and then it tries to evaluate its effectiveness (in terms of correctness and efficiency) in solving the corrective maintenance problem. Experimental results show that when the corrective maintenance task is assisted by user-centricvisualizations from a real-time Digital Twin, it significantly improved the accuracy and efficiency of the maintenance technician by about 24% and 52,4% respectively. Further, a post-experimental qualitative analysis explains that it is not any visualization but a Digital Twin based data-driven visualization, built on the user requirements that helped perform the corrective maintenance task more effectively.<br>Denna avhandling utvärderar betydelsen av den datadrivna lösningen som baseras på en Digital Tvilling vilken hjälper korrigerande underhållstekniker att utnyttja sina tvärvetenskapliga tekniska färdigheter vid åtgärdande av komplexa mekatroniska problem. Med anledning av felens komplexa mekatroniska natur är mänsklig inblandning nödvändig vid korrigerande underhåll. Även idag utför många industrier korrigerande underhåll genom att använda metoder som både är tidsineffektiva och felbenägna. Mjukvaru- eller AI-baserade lösningar har vida rapporterats ha misslyckats med anledning av ett försummande av den mänskliga aspekten i underhåll. Än så länge kan inte den mänskliga rollen helt ersättas av mjukvarusystem. Standard praxis för underhåll såsom FMEA och RCA är kostsamma, tidskrävande och känsliga för fel. Å andra sidan har lösningar som baserats på en Digital Tvilling (DT) visat på förbättrad hantering och effektivitet av underhåll genom att ta hänsyn till den mänskliga aspekten. För korrigerande underhåll är lösningen dock fortfarande i sitt konceptuella skede. Det finns ett behov av att praktiskt implementera en lösning baserad på en Digital Tvilling och kvantitativt utvärdera dess betydelse. Nyligen utförda studier har visat att ett Digtal Tvilling-koncept, uppbyggt på ett modellbaserat tillvägagångssätt, har en enorm potential i att tillhandahålla alla nödvändiga data som krävs för att kontrollera beteendet hos ett nätverk av fysiska enheter och samtidigt virtuellt övervaka deras verkliga tillstånd effektivt. Denna avhandling försöker först utveckla användarcentrerade visualiseringar uppbyggda på en helt integrerad Digital Tvilling av ett komplext Cyberfysiskt Produktionssystem (Cyber Physical Production System CPPS) och försöker sedan utvärdera dess effektivitet (när det gäller korrekthet och effektivitet) för att lösa problemet vid korrigerandeunderhåll. Experimentella resultat visar att när uppgiften vid korrigerande underhåll assisteras av användarcentrerade visualiseringar från en i realtid motsvarande Digital Tvilling förbättrades underhållsteknikerns noggrannhet och effektivitet med cirka 24% respektive 52,4%. Vidareförklarar en post-experimentell kvalitativ analys att det inte är vilken visualisering som helst utanen datadriven visualisering baserad på en digital tvilling och uppbyggd på användarkrav som hjälpte till att utföra uppgiften för korrigerande underhåll mer effektivt.

Given recent advancements in technology and recognizing the evolution of smart manufacturing, the implementation of digital twins for factories and processes is becoming more common and more useful. Additionally, expansion in connectivity, growth in data storage, and the implementation of the Industrial Internet of Things (IIoT) allow for greater opportunities not only with digital twins but closed loop analytics. Discrete Event Simulation (DES) has been used to create digital twins and in some instances fitted with live connections to closely monitor factory operations. However, the benefits of a connected digital twin are not easily quantified. Therefore, a test bed demonstration factory was used, which implements smart technologies, to evaluate the effectiveness of a closed-loop digital twin in identifying and reacting to trends in production. This involves a digital twin of a factory process using DES. Although traditional DES is typically modeled using historical data, a DES system was developed which made use of live data with embedded machine learning to improve predictions. This model had live data updated directly to the DES model without user interaction, creating an adaptive and dynamic model. It was found that this DES with machine learning capabilities typically provided more accurate predictions of future performance and unforeseen near future problems when compared to the predictions of a traditional DES using only historic data

COVID-19 has led to a surge in the adoption of digital transformation in almost every sector. Digital health and well-being are no exception. For instance, now people get checkupsvia apps or websites instead of visiting a physician. The pandemic has pushed the health-care sector worldwide to advance the adoption of artificial intelligence (AI) capabilities.Considering the demand for AI in supporting the well-being of an individual, we presentthe real-life diagnosis as a digital twin(DT) diagnosis using machine learning. The MachineLearning (ML) technology enables DT to offer a prediction. Although several attemptsexist for predicting disease using ML and a few attempts through ML of DT frameworks,those do not deal with disease risk prediction. In addition, most of them deal with singledisease prediction after the occurrence and rely only on clinical test data like- ECG report,MRI scan, etc.To predict multiple disease/disease risks, we propose a dynamic machine learning algo-rithm (MLA) selection framework and a dynamic testing method. The proposed frameworkaccepts heterogeneous electronic health records (EHRs) or digital health status as datasetsand selects suitable MLA upon the highest similarity. Then it trains specific classifiers forpredicting a specific disease/disease risk. The dynamic testing method for prediction isused for predicting several diseases.We described three use cases: non-communicable disease(NCD) risk prediction, mentalwell-being prediction, and COVID-19 prediction. We selected diabetes, risk of diabetes,liver disease, thyroid, risk of stroke as NCDs, mental stress as a mental health issue, andCOVID-19. We employed seven datasets, including public and private datasets, with adiverse range of attributes, sizes, types, and formats to evaluate whether the proposedframework is suitable to data heterogeneity. Our experiment found that the proposed methods of dynamic MLA selection could select MLA for each dataset at cosine similarityscores ranging between 0.82-0.89. In addition, we predicted target disease/disease risks atan accuracy ranging from 94.5% to 98%.To verify the performance of the framework-selected predictor, we compared the accuracy measures individually for each of the three cases. We compared them with traditionalML disease prediction work in the literature. We found that the framework-selected algorithms performed with good accuracy compared to existing literature.

Digital Twin is a concept that refers to a virtual representation of manufacturing elements suchas personnel, products, assets and process definitions, a living model that continuously updatesand changes as the physical counterpart changes to represent status, working conditions, productgeometries and resource states in a synchronous manner [1]. The digital representation providesboth the elements and the dynamics of how a physical part operates and lives throughout its lifecycle.In recent years, digital twin caught the attention of many researchers, who investigated its adoptionin various fields. In this thesis, we report on the planning, execution and results of a systematicmapping study to examine the current application of the digital twin, its research relevance, application domains, enabling technologies and perceived benefits. We start from an initial set of 675publications and through a rigorous selection process we obtain a final set of 29 primary studies.Using a classification framework, we extract relevant data. We analyse the extracted data usingboth quantitative and qualitative analyses using vertical and orthogonal analysis techniques.This work is aimed to investigate the current achievements of Digital Twin with the focus on revealing technologies it uses, its applications and benefits it offers by implementing it as well aspublication details.

<p> There is a need for a foundation of a research study aimed at investigations on near real-time reliability awareness of Gallium Nitride devices in high-frequency power converters for which we need advanced hardware and algorithms. This dissertation is moving beyond traditional reliability analysis and looking to more applicable and accurate analytical tools by introducing deep learning techniques and advanced sensing solutions. The computational structures will be applied at the edge of the power converter through online sensing and data processing units as well as on a remote server. They will provide an iterative ability to predict the time until the device may fail or reach a pre-defined degradation threshold. </p><p> With the availability of the most granular information deduced from advanced devices, a new data-driven scheme is proposed for system monitoring and possible lifetime extension Gallium Nitride power converters. The approach relies on the real-time on-resistance data extraction from the power converter, and calibration of an adaptive model using multi-physics co-simulations under power cycling. More specifically, the focus is on deploying machine learning algorithms to exploit for the parameter estimation in power electronics engineering reliability. The proposed techniques in this work are quite new and have not yet been developed and analyzed for high-frequency power converters specifically with Gallium Nitride power semiconductor devices.</p><p>

All'interno del mondo dell'industria 4.0, negli ultimi anni è stato introdotto un elemento molto importante che serve alle imprese per ridurre ulteriormente i costi interni e per massimizzare il profitto, il Digital Twin. Il digital twin permette di poter creare un'astrazione dell'oggetto reale all'interno del mondo digitale e con questa astrazione è possibile fare previsioni su comportamenti futuri o su possibili problematiche che potrebbero affliggere l'elemento in questione. Lo scopo principale di questo documento è studiare se i digital twin, elementi importantissimi nel campo industriale, possano essere importati negli altri ambiti della società moderna, portando effettivamente ad un potenziamento di ciò che la società attualmente offre, affrontando nello specifico l'ambito ospedaliero e proponendo un modello tramite il quale è possibile includere il digital twin all'interno di questo contesto.

A digital twin is an enabling technology that facilitates monitoring, understanding, and providing continuous feedback to improve quality of life and well-being. Thus, a digital twin can consider a solution to enhance one's mood to improve the quality of life and emotional well-being. However, there remains a long road ahead until we reach digital twin systems that are capable of empowering development and the deployment of digital twins. This is because there are so many elements and components that can guide the design of a digital twin. This thesis provides a general discussion for the central element of an emotional digital twin, including emotion detection, emotional biofeedback, and emotion-aware recommender systems. In the first part of this thesis, we propose and study the emotion detection models and algorithms. For emotions, which are known to be highly user dependent, improvements to the emotion learning algorithm can significantly boost its predictive power. We aimed to improve the accuracy of the classifier using peripheral physiological signals. Here, we present a hybrid sensor fusion approach based on a stacking model that allows for data from multiple sensors and emotion models to be jointly embedded within a user-independent model. In the second part of this thesis, we propose a real-time mobile biofeedback system that uses wearable sensors to depict five basic emotions and provides the user with emotional feedback. These systems apply the concept of Live Biofeedback through the introduction of an emotion-aware digital twin. An essential element in these systems guides users through an emotion-regulation routine. The proposed systems are aimed at increasing self-awareness by using visual feedback and provide insight into the future design of digital twins. We focus on workplace environments, and the recommendations are based on human emotions and the regulation of emotion in the construct of emotional intelligence. The objective is to suggest coping techniques to a user during an emotional, stressful episode based on her or his preferences, history of what worked well and appropriateness for the context. The developed solution has been studied based on usability studies and extensively compared to related works. The obtained results show the potentials use as an emotional digital twin. In turn, the proposed solution has been providing significant insights that will guide future developments of digital twins using several scenarios and settings.