Search

In this study, a digital twin for a flat plate solar collector field is proposed. This kind of system is used to reduce carbon dioxide emissions in bioclimatic buildings to convert them into Zero Energy Buildings. The core of the digital twin is an Artificial Neural Network prediction model, which is a good alternative to models based on physical equations for modeling systems with strong non-linearities, such as the ones found in flat plate solar collectors. The Artificial Neural Network prediction model is calibrated and validated with data saved during one year of operation comprising sunny days, cloudy days, partially cloudy days and non-operation days. Validation shows good results using several statistical metrics, suggesting that the Artificial Neural Network model is suitable for operation and control purposes. With a highly accurate virtual representation, the Artificial Neural Network model allows data analysis of the plant operator, prediction of behavior, and offers recommendations for optimizing system performance. In addition, the digital twin presented as part of this work is not just limited to the model, but is also enriched by the integration of data acquisition technologies and a user interface into a web page. This innovative integration establishes a robust framework for proactive, real-time decision-making and efficient management of the plant, ensuring enhanced system operation and sustainability. • Reducing CO 2 emissions and energy dependence through renewable energy integration. • Digital twins for real-time monitoring, analysis, and fault detection in complex energy systems. • Bioclimatic building with an HVAC system based on solar cooling and a flat plate solar collector field. • ANN model for collector field temperature integrated into a web-based digital twin for real-time monitoring and fault-tolerant management. • Testing of various ANN configurations, with the selected model integrated into the digital twin. [ABSTRACT FROM AUTHOR]

Digital twin (DT)-driven intelligent fault diagnosis (IFD) has been a hot topic, which can support personalized monitoring of critical machinery. A central challenge is that diagnostic models using deep learning (DL) suffer from the problem of catastrophic forgetting if personalized faults occur in dynamic environments. To deal with this issue, this article presents a class-incremental learning method with multi-fidelity information fusion (MFIF-CIL) for the continuous diagnosis of key faults in rolling bearings. First, an effective bearing DT model is constructed to generate enough low-fidelity (LF) simulation data. Second, feature boosting is developed to fit the residuals with distribution drifts between old classes and new classes, which helps prevent the problem of catastrophic forgetting. Last, the MFIF module is proposed for multi-fidelity knowledge transfer and fusion to leverage LF simulation data to improve the class-incremental learning ability of feature boosting with limited high-fidelity (HF) physical data. The testing datasets consisting of the measured signals are utilized as testing datasets of optimal incremental neural networks for fault diagnosis. The proposed MFIF-CIL-1 (using 15 HF data and 100 LF data as exemplars) and MFIF-CIL-2 (using 20 HF data and 100 LF data as exemplars) obtain the average diagnostic accuracies of 96.87% and 98.10%, respectively. The MFIF-CIL-2 only uses 41.93% of the training time required by the joint training method. These satisfying results demonstrate that the MFIF-CIL can effectively diagnose different health conditions over time and provide a tradeoff between relatively low experimental costs and high accuracy. • A digital twin model is developed for the dynamic modeling of bearings. • Feature boosting mechanism is used to overcome catastrophic forgetting. • Multi-fidelity information fusion for reduction of the experimental costs. • One laboratory dataset is used to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this study, a digital twin for a flat plate solar collector field is proposed. This kind of system is used to reduce carbon dioxide emissions in bioclimatic buildings to convert them into Zero Energy Buildings. The core of the digital twin is an Artificial Neural Network prediction model, which is a good alternative to models based on physical equations for modeling systems with strong non-linearities, such as the ones found in flat plate solar collectors. The Artificial Neural Network prediction model is calibrated and validated with data saved during one year of operation comprising sunny days, cloudy days, partially cloudy days and non-operation days. Validation shows good results using several statistical metrics, suggesting that the Artificial Neural Network model is suitable for operation and control purposes. With a highly accurate virtual representation, the Artificial Neural Network model allows data analysis of the plant operator, prediction of behavior, and offers recommendations for optimizing system performance. In addition, the digital twin presented as part of this work is not just limited to the model, but is also enriched by the integration of data acquisition technologies and a user interface into a web page. This innovative integration establishes a robust framework for proactive, real-time decision-making and efficient management of the plant, ensuring enhanced system operation and sustainability. • Reducing CO 2 emissions and energy dependence through renewable energy integration. • Digital twins for real-time monitoring, analysis, and fault detection in complex energy systems. • Bioclimatic building with an HVAC system based on solar cooling and a flat plate solar collector field. • ANN model for collector field temperature integrated into a web-based digital twin for real-time monitoring and fault-tolerant management. • Testing of various ANN configurations, with the selected model integrated into the digital twin. [ABSTRACT FROM AUTHOR]

Digital twin (DT)-driven intelligent fault diagnosis (IFD) has been a hot topic, which can support personalized monitoring of critical machinery. A central challenge is that diagnostic models using deep learning (DL) suffer from the problem of catastrophic forgetting if personalized faults occur in dynamic environments. To deal with this issue, this article presents a class-incremental learning method with multi-fidelity information fusion (MFIF-CIL) for the continuous diagnosis of key faults in rolling bearings. First, an effective bearing DT model is constructed to generate enough low-fidelity (LF) simulation data. Second, feature boosting is developed to fit the residuals with distribution drifts between old classes and new classes, which helps prevent the problem of catastrophic forgetting. Last, the MFIF module is proposed for multi-fidelity knowledge transfer and fusion to leverage LF simulation data to improve the class-incremental learning ability of feature boosting with limited high-fidelity (HF) physical data. The testing datasets consisting of the measured signals are utilized as testing datasets of optimal incremental neural networks for fault diagnosis. The proposed MFIF-CIL-1 (using 15 HF data and 100 LF data as exemplars) and MFIF-CIL-2 (using 20 HF data and 100 LF data as exemplars) obtain the average diagnostic accuracies of 96.87% and 98.10%, respectively. The MFIF-CIL-2 only uses 41.93% of the training time required by the joint training method. These satisfying results demonstrate that the MFIF-CIL can effectively diagnose different health conditions over time and provide a tradeoff between relatively low experimental costs and high accuracy. • A digital twin model is developed for the dynamic modeling of bearings. • Feature boosting mechanism is used to overcome catastrophic forgetting. • Multi-fidelity information fusion for reduction of the experimental costs. • One laboratory dataset is used to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Neural Operator Networks (ONets) represent a novel advancement in machine learning algorithms, offering a robust and generalizable alternative for approximating partial differential equations (PDEs) solutions. Unlike traditional Neural Networks (NN), which directly approximate functions, ONets specialize in approximating mathematical operators, enhancing their efficacy in addressing complex PDEs.In this work, we evaluate the capabilities of Deep Operator Networks (DeepONets), an ONets implementation using a branch–trunk architecture. Three test cases are studied: a system of ODEs, a general diffusion system, and the convection–diffusion Burgers' equation. It is demonstrated that DeepONets can accurately learn the solution operators, achieving prediction accuracy (R 2) scores above 0.96 for the ODE and diffusion problems over the observed domain while achieving zero-shot (without retraining) capability. More importantly, when evaluated on unseen scenarios (zero-shot feature), the trained models exhibit excellent generalization ability. This underscores ONets' vital niche for surrogate modeling and digital twin development across physical systems. While convection–diffusion poses a greater challenge, the results confirm the promise of ONets and motivate further enhancements to the DeepONet algorithm. This work represents an important step towards unlocking the potential of digital twins through robust and generalizable surrogates. [ABSTRACT FROM AUTHOR]

The healthcare sector has been revolutionized by Information and Communication Technology (ICT), leading to increased patient life expectancy and reduced healthcare costs. In the realm of cutting-edge research, Digital Twins (DT) technology holds great promise for improving healthcare. This study introduces a new approach to securing adult healthcare data by combining the Internet of Things (IoT), DT technology, and blockchain technology. Specifically, a context-aware physical activity monitoring framework is proposed for adult healthcare, incorporating an Artificial Intelligence-inspired Convolutional Neural Network (CNN) technique to analyze real-time abnormalities in the elderly. The CNN is trained on a large dataset, learning to recognize patterns and anomalies associated with abnormal conditions or behaviors in the elderly. The framework also ensures data security through the advanced features of blockchain, employing the Reputation-based Byzantine Fault Tolerance (RBFT) method for the consortium network. Experimental simulations validate the proposed technique, demonstrating its superior efficacy compared to state-of-the-art techniques. The results exhibit betters statistical measures of Delay Latency (121.23s), Prediction Efficacy (Precision (93.357%), Specificity (93.58%), Sensitivity (94.15%), and F-measure (94.58%)), Reliability (89.62%), and Stability (71%). [ABSTRACT FROM AUTHOR]

With the development of mobile Internet and the popularity of public online consumption, the scale expansion of the e-commerce industry has driven the continuous growth of logistics business, but the increase in order volume has brought pressure and challenges to the end of logistics and distribution, and the emergence of crowdsourcing logistics It provides a new path for alleviating the current logistics and transportation dilemma. The Digital Twin (DT) can virtualize and learn the data of the physical space, and introduce DT into the crowdsourcing logistics. It can iteratively update the crowdsourcing logistics participant strategy by constructing a virtual space, so as to change the corresponding strategy in time. Considering the situation of crowdsourcing logistics workers signing contracts with platforms and colluding with platforms, this paper constructs a four-party evolutionary game model of temporary workers, contract workers, blockchain-based crowdsourcing platforms and task requester, and analyzes the evolution using replication dynamics method stabilization strategy. In the virtual scene of DT, multi-agent reinforcement learning is used to predict the evolution result of the current strategy, and a reward and punishment strategy is given to prevent workers from free-riding and platform false reporting. The simulation results show that the analyzed evolutionary stability strategy can make the crowdsourcing logistics system run continuously and healthily, and the participants can adjust the strategy correctly according to the prediction results of virtual crowdsourcing logistics. [ABSTRACT FROM AUTHOR]

The dynamic and complex environment affects the operational efficiency of automated guided vehicles (AGVs) at automated container terminals, in which artificial intelligent approaches are applied to address optimization problems in dynamic systems. This paper proposes a digital twin-based decision support approach to improve the efficiency of AGV scheduling service. Accordingly, the factors that affect AGV scheduling performance, including conflicts, failures, and battery constraints, are discussed first. Then, the framework and main steps of the proposed approach are described. The physical operation space is mapped into the virtual space, and both spaces keep synchronized to support the verification of solutions. A mathematical programming model and Q-learning algorithm are used to generate the AGV scheduling plan considering battery charging. Numerical experiments are conducted to demonstrate the effectiveness of the proposed approach. Comparisons of the digital twin-based approach, genetic algorithm (GA), and particle swarm optimization (PSO) are also made with different scale experiments. It appears that the proposed digital twin-based approach is superior to GA and PSO when solving small- and large-scale cases. A sensitivity analysis is performed concerning the battery utilization rate, task, and AGV number. Experimental results show that an optimal configuration of AGV and task can improve the battery utilization rate and reduce the completion time. • A digital twin-based decision support approach is proposed to improve scheduling. • A mathematical programming model is developed to minimize the total completion time. • Digital twin and Q-learning are used to monitor operation and obtain solutions. • Sensitivity analysis of battery utilization is made to provide equipment maintenance. • The proposed approach is superior based on the comparison of solution performances. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and Machine learning (ML) are increasingly used for digital twin development in energy and engineering systems, but these models must be fair, unbiased, interpretable, and explainable. It is critical to have confidence in AI's trustworthiness. ML techniques have been useful in predicting important parameters and improving model performance. However, for these AI techniques to be useful in making decisions, they need to be audited, accounted for, and easy to understand. Therefore, the use of explainable AI (XAI) and interpretable machine learning (IML) is crucial for the accurate prediction of prognostics, such as remaining useful life (RUL), in a digital twin system to make it intelligent while ensuring that the AI model is transparent in its decision-making processes and that the predictions it generates can be understood and trusted by users. By using an explainable, interpretable, and trustworthy AI, intelligent digital twin systems can make more accurate predictions of RUL, leading to better maintenance and repair planning and, ultimately, improved system performance. This paper aims to explain the ideas of XAI and IML and justify the important role of AI/ML for the digital twin components, which requires XAI to understand the prediction better. This paper explains the importance and fundamentals of XAI and IML in both local and global aspects in terms of feature selection, model interpretability, and model diagnosis and validation to ensure the reliable use of trustworthy AI/ML applications for RUL prediction. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to digitizing and modeling pressure swing adsorption (PSA) processes using an uncertainty-aware digital twin. PSA modeling presents unique challenges due to its complex and cyclic behavior, which lacks a steady state. By contributing to the literature on periodic systems, we provide valuable insights into the potential applications of artificial intelligence and digital twins beyond the field of cyclic processes. Our proposed methodology can enhance the understanding and optimization of complex systems across various industries and applications. The proposed digital twin is uncertainty-aware and reliable, continuously updating itself through online learning and utilizing a novel feedback tracker to accurately represent the PSA system. This robust and adaptable methodology supports optimal PSA system operation and facilitates informed decision-making for enhanced process operation. The results demonstrate that the proposed approach yields a reliable digital twin for the PSA unit, capable of tracking the process's complex dynamics and adapting to changes, including adsorbent degradation, which is a significant challenge in PSA operations. Overall, this work highlights the potential of advanced technologies, such as digital twins and artificial intelligence, to improve performance and efficiency in the field of process engineering. This work contributes to the ongoing efforts to optimize industrial processes and support sustainable development by providing a reliable and adaptable methodology for digitizing PSA processes. • Digital twin for a PSA system in syngas purification. • Online learning and uncertainty assessment to address uncertainty identification and computational feasibility. • Robust and adaptable digital twin methodology. [ABSTRACT FROM AUTHOR]

Early failure detection is required for Fused Deposition Modelling (FDM) 3D printers to reduce material waste. Typically, such systems are created based on images captured during printing or sensor data for tracking the extruder's movement. This work presents a novel approach to sensor data-driven fault diagnosis, utilizing Artificial Intelligence (AI) technology to investigate the temperature imbalance in the extruder and printing surface. First, a Lightweight Convolutional Neural Network (LCNN) is proposed to detect faults from sensory data. The model's architecture concatenates the CNN layer to extract additional features, improving the model's performance while maintaining a lightweight configuration suitable for real-time monitoring systems. Second, the concept of Digital Twin (DT) technology for FDM 3D printer fault detection is introduced. The DT creates a virtual representation of a physical object, and its functionality is validated by examining the network's latency and System Overhead (SO) as the number of clients increases. The simulation results show that the proposed LCNN with a DT environment can effectively monitor, detect, and control the physical workplace with an F1-Score of 0.9981 and an average latency of 995. 4253 ms. Additionally, this research contributes to the development of future technologies for virtual condition monitoring of 3D printer abnormalities, which will be essential for intelligent and autonomous factories. [ABSTRACT FROM AUTHOR]

Unmanned Air Vehicles (UAVs), i.e. drones, have become a key enabler technology of many reconnaissance applications in different fields, such as military, maritime, and transportation. UAVs offer several benefits, such as affordability and flexibility in deployment. However, their limited flight time due to energy consumption is one of the key limitations. Therefore, it is crucial to ensure that UAVs can complete the mission while consuming the least energy possible. In this paper, we propose a novel framework for UAV smart navigation to minimize the time and energy of planning mobile targets visitation. We develop a Deep Reinforcement Learning (DRL) approach to allow the drone to learn the targets' mobility pattern and build its least energy scanning strategy accordingly. We conduct an initial evaluation of the system and our proposed DRL model policy using simulation. Then, to overcome the time-consuming exploration phase of DRL, we develop a Digital Twin (DT) environment of 3D physics-based simulator, which can be used to train the DRL agent efficiently. We also developed a testbed based on hardware integration with the parrot ANAFI drone to verify the feasibility of the proposed methodology. Our findings confirm that the DRL-based agent can achieve performance close to that of a benchmark policy. Moreover, the testbed experiment validates the practicality of utilizing the DT environment for DRL exploration. [ABSTRACT FROM AUTHOR]

Automation is beneficial when implemented in challenging environments requiring less human effort or reducing human effort. Employee safety is a concern to increase productivity, delivery and achieve the required quality. The present research investigates the possibility of installing an automation procedure in an anodizing industry called GOLDEN ANODIZER, which is situated in Coimbatore, India. During the preliminary analysis at the specified industry premises, there was a reduction in productivity, quality and customer delivery due to manual operations, including some health issues were identified. And hence the present investigation analyses the possibility of installation of an automation system for the anodizing process. For such reason, the digital twin of an automation system is first designed, developed and tested using Siemens NX software. The anodizing factors such as Anodizing medium temperature (K), Acid Concentration (wt%), applied voltage (V) and Responses Surface Finish (Ra), Film Thickness (tf) and Time Duration (T) were optimized for improved productivity and quality. Prediction results and RMSE analysis show that the proposed hybrid PSO-LFA algorithm has outperformed all modern algorithms. The proposed algorithm optimized the anodizing parameters and suggested 3650.7 s as a new cycle time. Also, the improved cycle time can boost the plant outcome by 182% and reduce the manpower by 46% through the proposed Automation system. • Issues in the Aluminium anodizing industry due to the manual operation were studied. • An automatic anodizing plant layout based on the Digital Twin method was presented. • Enhanced accuracy during prediction and optimization by hybrid PSO-LVA. • 182% increased production with 46% lesser manpower is possible by automation. [ABSTRACT FROM AUTHOR]