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With significant advancement in information technologies, Digital Twin has gained increasing attention as it offers an enabling tool to realise digitally-driven, cloud-enabled manufacturing. Given the nonlinear dynamics and uncertainty involved during the process of machinery degradation, proper design and adaptability of a Digital Twin model remai...
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... an evolving digital replica of a physical system, Digital Twin becomes a key enabling technology for cybernetic manufacturing as shown in Figure 1. Digital Twin not only can be used for modelling and simulation of system development to support design or to validate system properties ( Schleich et al. 2017;Tao et al. 2018) but also can support the operation and manufacturing service for optimised operations and failure prediction. ...
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... to the constructed Digital Twin rotor model in Section 4.2, the parameters of three unbalance faults of the rotor system can be identified by the modal expansion method as shown in Figure 10. The equivalent load increases with the increase of unbalance mass at the seventh node. ...
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... is a common fault in rotating machinery, and it has been widely investigated using signal processing techniques, such as spectrum analysis, and time-frequency analysis. The analysis results of the measured vibration signals using Fourier transform and wavelet transform are shown in Figure 11. It can be found that the amplitudes of spectrum energy at rotating speed are elevated under the different unbalance fault conditions. ...
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... an evolving digital replica of a physical system, Digital Twin becomes a key enabling technology for cybernetic manufacturing as shown in Figure 1. Digital Twin not only can be used for modelling and simulation of system development to support design or to validate system properties ( Schleich et al. 2017;Tao et al. 2018) but also can support the operation and manufacturing service for optimised operations and failure prediction. ...
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... to the constructed Digital Twin rotor model in Section 4.2, the parameters of three unbalance faults of the rotor system can be identified by the modal expansion method as shown in Figure 10. The equivalent load increases with the increase of unbalance mass at the seventh node. ...
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... is a common fault in rotating machinery, and it has been widely investigated using signal processing techniques, such as spectrum analysis, and time-frequency analysis. The analysis results of the measured vibration signals using Fourier transform and wavelet transform are shown in Figure 11. It can be found that the amplitudes of spectrum energy at rotating speed are elevated under the different unbalance fault conditions. ...
Citations
... According to Liu et al., (2021), DTs have been deployed for actual control and monitoring of production processes, prediction of performance and maintenance, human-robot interactions, optimization, asset management, fault detection and diagnosis, as well as virtual tests. Other applications include analysis of rotating machines' performance (Wang et al., 2019), health management and prognostics (Tao et al., 2018). ...
... Xu et al. [10] introduce a method utilizing deep transfer learning for diagnosing faults early in the design phase, with a case study confirming its effectiveness in an automobile body-side production line. Finally, Wang et al. [11] present a digital twin model for diagnosing faults in rotating machinery, featuring an updated model based on parameter sensitivity analysis to enhance adaptability. The limitation of the methodologies presented lies in the absence of signal processing for denoising. ...
Energy production is essential to a country's progress and industrial advancement, particularly in the context of smart industry. Nature provides a variety of forms of energy, often harnessed by generators connected to gearboxes for energy conversion or speed regulation. Various faults, like chipped, worn, cracked, or broken teeth, can affect gearboxes. Implementing a fault monitoring and diagnosis system in real-time is imperative to avoid unplanned shutdowns and protect operators. This paper introduces a digital twin approach for gearbox fault diagnosis that anticipates fault signatures using healthy state monitoring signals. This approach removes the need for destructive testing to simulate various faults while addressing practical constraints in industry such as missing gearbox parameters, imperfections in mechanical realization, and acquisition noise. The result is a high-fidelity digital twin capable of real-time fault diagnosis. This flexible and universal solution can be applied to gearboxes by providing a few easily accessible parameters alongside the healthy state monitoring signal. The process begins with meshing modeling, which employs an energy-based method to determine the different energies involved in determining mesh stiffness for both the optimal condition and different types of faults. Stiffness signals are then integrated into a 6DOF dynamic vibration model to generate vibration signal responses. Subsequently, the adjustment of model parameters to experimental data sets allows the alignment of model vibration signals. Finally, variational mode decomposition (VMD) is used to dissect experimental vibration signals, separating mesh signatures from noise related to bench imperfections and acquisition. This noise is then added to the modeled faults to emulate the experimental vibration signals. The similarity between the experimental and emulated vibration signals is evaluated using the Pearson coefficient, demonstrating the effectiveness of the proposed approach and facilitating the development of a gearbox twin for fault diagnosis.
... Wang et al. [76] designed a DT model for fault diagnosis of rotating machinery is proposed. ...
In Industry 5.0, where human ingenuity is combined with cutting-edge technologies such as artificial intelligence (AI) and robotics to revolutionize manufacturing with a focus on sustainability and human well-being, Digital Twins (DT) have become essential for real-time optimization. However, the complexity of managing DT for large-scale systems poses challenges in terms of data transmission, analytics, and advanced applications, which can be potentially addressed by Large Language Model (LLM). This research firstly performs a literature review to study the roles and functions of LLM in DT in the context of Industry 5.0. Subsequently, we propose a framework named Interactive-DT for LLM-DT integration that reveals the technical pathway for how LLM can be effectively integrated and function within DT environments. Within this framework, the roles and functionalities of LLM at the edge layer, DT layer, and service layer are elaborated upon. Finally, the identified research gaps and prospects for the integration of LLM and DT are outlined and discussed. The research outcomes of this paper highlight the potential of LLM to augment DT capabilities through improved construction and operation, enhanced cloud-edge collaboration, and sophisticated data analytics, ultimately promoting industrial practices that are both efficient and aligned with human-centric and sustainability principles in Industry 5.0.
... It represents an advanced version of virtual simulation models that integrate real and cyber environments to simulate real-world components, reflecting information models and functional elements [37][38][39]. Söderberg et al. defined a DT as a model capable of real-time control and optimization through data, algorithms, and simulations [40], whereas Wang et al. highlighted its support for technologies such as machine learning and cloud services [41]. Compared with traditional simulation models, DT offers significant advantages, including real-time data integration, bidirectional communication between physical and digital systems, and adaptive behavior through machine learning algorithms. ...
Manufacturing supply chains are becoming increasingly complex due to geopolitical issues, globalization, and market demand uncertainties. These challenges lead to logistics disruptions, inventory shortages, and interruptions in raw materials and spare parts production, resulting in delayed delivery, reduced market share, and lower customer satisfaction. Effective supply chain management is critical for improving operational efficiency and competitiveness. This paper proposes a supply chain digital twin methodology to enhance operational efficiency through real-time monitoring, analysis, and response to disruptions. This methodology defines a supply chain digital twin system architecture and outlines the operational process of digital twin applications. It introduces two key modules: a digital twin module for prediction and monitoring and an optimization module for determining the optimal movement of products. These modules are integrated to align digital simulations with real-world supply chain operations. The proposed approach is validated through a case study of an automobile body production company’s supply chain, demonstrating its effectiveness in reducing inventory and logistics costs while providing countermeasures for abnormal situations.
... Table 1 provides a summary of earlier research in this field. A novel DT model for rotating equipment based on an update scheme parameter model was given by Wang et al. in [29]. These suggested frameworks are successful in improving the diagnosis and prediction of imbalanced faults [30]. ...
... Implementation services of satellite system integration. [29] DT system which is used for rotating machinery systems for fault diagnosis ...
Digital Twin has emerged as a transformative technology within the Industrial Internet of Things, addressing the pressing need for accurate, real-time fault detection in critical industrial applications. Leveraging Artificial Intelligence, Digital Twin enables near-real data generation from physical assets, enhancing fault detection capabilities in complex environments. This study addresses the challenge of reliable, efficient fault detection by proposing a novel intelligent diagnostic model for Digital Twin systems using a hybrid ensemble machine learning approach optimized through a genetic algorithm. The model combines Voting Ensemble Learning with genetic algorithm-based optimization to improve fault classification performance under real-time conditions. Experimental results reveal that our hybrid Voting-genetic algorithm-ensemble learning model achieves superior fault detection accuracy, with performance metrics reaching 88.2% and 98.2% for datasets A and B, respectively, outperforming traditional methods such as classification and regression tree and random forest, which obtained lower accuracy rates (69.5%, 77.8%, 75%, and 73%). Additionally, the proposed model demonstrates substantial improvements over other advanced models, including those that utilize feature selectionwith metaheuristic algorithms and genetic algorithm-tuned machine learning approach. This framework not only enhances diagnostic accuracy but also offers robust and scalable fault detection in cyber-physical and IoT-enabled industrial environments, establishing a new standard for fault diagnosis in Digital Twin applications.
... This helps assess model applicability across scales. Sensitivity analysis on digital twin models [135,140] determines the stability and reliability of models. Developing standards and metrics for multi-scale validation ensures that digital twin models meet quality and application standards. ...
The evolution of manufacturing towards intelligent and digital processes requires innovation in machining quality control. While current research primarily addresses single-scale quality control, it overlooks comprehensive multi-scale product quality characterization. Digital twin technology emerges as a potential solution. This review examines digital twin applications in machining quality control, highlighting limitations of traditional methods and exploring multi-scale quality characterization at macro, meso, and micro levels. It evaluates multi-scale quality changes during processing and summarizes comprehensive characterization methods across scales. The study concludes by discussing future prospects for digital twin technology in multi-scale machining quality control and optimization.
... Research in [234] indicates that about 18% of DT's manufacturing applications are focused on the design space, followed by production areas (35%), health management and prognostics (38%), and other areas (9%). Well-known DT applications for smart manufacturing include product design, production scheduling, problem detection, and predictive maintenance [295], [258], [277], [233], [210], [93], that are intended for small-scale real physical systems. In most manufacturing DT applications, the latency of data transmission between the physical system and the DT can be decreased by placing the DTs close to the physical systems. ...
Network Digital Twin (NDT) is an evolving technology that provides a framework through which a network administrator can have a virtual representation of a computer network. As a result, analysis, monitoring, testing, running new protocols, and more can be performed using the NDT before the final deployment of the developed approach. In this way, the consequences of direct deployment and the negative impact on network operations can be avoided. Telecommunications, along with traffic engineering as one of its critical components, play a prominent role across various networking domains, including Internet service providers, data centers, cellular networks, intelligent transportation systems, and smart cities. In this context, NDT has the potential to serve as a key enabler for optimizing these domains by providing a digital framework, which can facilitate the evaluation and enhancement of scenarios such as congestion management and routing strategies. Accordingly, this paper presents a comprehensive survey on how NDT can facilitate advancements in network traffic engineering across a wide range of networking domains. First, we start with an in-depth analysis of the evolution of the network digital twin technology and provide a comparison with simulation tools in order to make it feasible to select the right one based on the requirements. Next, we examine the role of NDT in various networking and telecommunication domains, including its potential as an enabler for 5G/6G networks, the Industrial Internet of Things (IIoT), smart manufacturing, intelligent transportation systems, and smart cities. We also explore the applicability of NDT technology from a traffic engineering perspective across different network types. Subsequently, we highlight key open research questions and potential future directions that warrant further investigation. Finally, we conclude by outlining the promising future trajectory of NDT within the aforementioned domains.
... However, it is still difficult to interpret the severity of the unbalance fault of the rotor system [10]. These approaches to machinery diagnostics are generic rather than machine-specific, and data interpretation is based on qualitative rather than quantitative information [11]. ...
... To overcome the limitation regarding the availability of fault data, mathematical models should be combined altogether with the above AI systems [26]. Jinjiang Wang et al. [11] estimate the unbalance in rotor-bearing systems (severity and position) using the equivalent load methodology combined with optimization algorithms. This method provides efficient online identification of malfunctions utilizing a digital twin methodology showing that that the constructed digital twin rotor model enables accurate diagnosis and adaptive degradation analysis. ...
Rotating systems are one of the most common systems that transfer power in several industrial sectors. Unbalance is a severe fault that contributes to machine downtime and unscheduled maintenance actions and can damage crucial rotary systems. Estimation of unbalance and model parameter update in rotor-bearing systems are crucial for the safe and efficient operation of the machine.
This research paper presents a novel approach utilizing artificial neural networks (ANNs) to identify the mass and location of unbalance in a multidisk system. Additionally, the study explores the model updating process of rotating system parameters using genetic algorithms (GAs).
The integration of physics-based models with artificial intelligence algorithms led to the development of digital twin-based methodologies for model parameter updating and unbalance estimation. For all the artificial neural networks of this paper, the coefficient of determination (R2) exceeded 0.99, indicating excellent predictive accuracy. Additionally, the implementation of genetic algorithms (GAs) consistently yielded objective function values below 5 × 10–4, enhancing the reliability of the developed systems. These results demonstrate the system’s potential for real-time application and its significant advantages in predictive maintenance.
The main novelty of this work is the development of digital twin methodologies, for unbalance estimation and parameter assessment, which consist of a mathematical model of the rotor, ANNs, GAs, and real-experimental vibration data from a rotor-kit. The obtained results demonstrate the system’s potential for real-time application and its advantages in predictive maintenance.
... dynamic interaction between the physical entity of the elevator and its digital twin. WANG J et al. 16 propose a digital twin reference model for fault diagnosis of rotating machinery. And the construction demands of the digital twin model were discussed. ...
The rapid development of urbanization has led to a continuous rise in number of elevators. This has led to elevator failures from time to time. At present, although there are some studies on elevator fault diagnosis, they are more or less limited by the lack of data to make the research more superficial. For such complex special equipment as elevator, it is difficult to obtain reliable and sufficient data to train the fault diagnosis model. To address this issue, this paper first establishes a numerical model of vertical vibration for elevators with three degrees of freedom. The obtained motion equations are then used as constraints to acquire simulated vibration data through PINNs. Next, the proposed e-RGCN is employed for elevator fault diagnosis. Finally, experimental validation shows that the fault diagnosis accuracy with the participation of digital twins exceeds 90%, and the accuracy of the proposed model reaches 96.61%, significantly higher than that of other comparative models.
... Wang Jinjiang proposed a DT model updating scheme based on parameter sensitivity analysis to enhance the adaptability of the model. He found through research that the constructed DT model can perform accurate diagnosis and adaptive degradation analysis [5]. Vijayakumar proposed a method of applying DT to manufacturing facilities to maintain real-time updates and simulations of models, in response to the issue of significant time and cost required for digital updates of current manufacturing facilities. ...
With the increasing digitalization of distribution network equipment (DNE), real‐time update algorithms for digital twin (DT) models have become a research focus on the digitalization of DNE. However, traditional real‐time update algorithms for DT models still have problems such as poor real‐time, accuracy, robustness, and scalability. To better promote the development of digitalization of DNE, this article aimed to study the real‐time update algorithm of DT models using the Internet of Things (IoT) and optical imaging technology, to achieve real‐time updates of DT models of DNE. The article first described the problems existing in the traditional DT model of DNE. Then, IoT sensors and optical devices were used to collect data related to DNE; the Savitzky–Golay filtering algorithm was used to denoise the data. This article combined the IoT and optical imaging technology to construct a DT model; using the recursive least squares method again, key parameters and state parameters were extracted from the constructed DT mechanism model, achieving real‐time updates of the DNE DT model. Finally, to verify the application effect of the IoT and optical imaging technology in real‐time update algorithms for DT models of DNE, this article compared them with traditional parameter sensitivity analysis and state estimation. The research results showed that in the real‐time and accuracy testing of test case 13, the algorithm used in this article had a time of 0.014 s and an accuracy of 93.2%. The parameter sensitivity analysis method had a time of 0.045 s and an accuracy of 80.4%. The state estimation method took 0.056 s and had an accuracy of 82.7%. In addition, the robustness and scalability of the real‐time update algorithm for the DNE DT model using the method proposed in this article were significantly better than the other two traditional methods. The results showed that the real‐time update algorithm of the DT model of DNE based on the IoT and optical imaging technology had better real‐time performance, higher accuracy, and better robustness and scalability. This study highlights the significant impact of the IoT and optical imaging technology on the accuracy, robustness, and real‐time performance of real‐time update algorithms for DT models. This provides more solutions for real‐time monitoring, prediction, and control of DNE.