Digital twin system design for dual-manipulator cooperation unit

Zhang, Zhishu; Lu, Jianfeng; Xia, Luyao; Wang, Sheng; Zhang, Hao; Zhao, Rongyong

doi:10.1109/itnec48623.2020.9084652

Cited by 32 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DTs are becoming increasingly relevant in the automotive sector due to their potential to address challenges at various stages of a vehicle's lifecycle. In the design phase, virtual prototyping and testing via DTs enable engineers to identify issues before physical prototypes are built [24]. During manufacturing, DTs optimize production processes, predict equipment failures, and reduce downtime [25].…”

mentioning

confidence: 99%

Review of Electric Vehicle Testing Procedures for Digital Twin Development: A Comprehensive Analysis

Rjabtšikov,

Rassõlkin,

Kudelina

et al. 2023

Energies

View full text Add to dashboard Cite

This article explores the transformative potential of digital twin (DT) technology in the automotive sector, focusing on its applications in enhancing propulsion drive systems. DT technology, a virtual representation of physical objects, has gained momentum due to its real-time monitoring and analysis capabilities. Within the automotive industry, where propulsion systems dictate vehicle performance, DTs offer a game-changing approach. Propulsion drive systems encompass electric motors, transmissions, and related components, significantly impacting efficiency and power delivery. Traditional design and testing methods need help addressing these systems’ intricate interactions. This article aims to investigate how DTs can revolutionize propulsion systems. The study examines various applications of DTs, ranging from predictive maintenance to performance optimization and energy efficiency enhancement. The article underscores the technology’s potential by reviewing case studies and real-world implementations. It also outlines challenges tied to integration and validation. In unveiling the capabilities of DT technology for propulsion systems, this article contributes to a comprehensive understanding of its role in shaping a more data-driven and efficient automotive industry.

show abstract

mentioning

confidence: 99%

Review of Electric Vehicle Testing Procedures for Digital Twin Development: A Comprehensive Analysis

Rjabtšikov,

Rassõlkin,

Kudelina

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

“…The basic assumption of the model is that each pharmaceutical company implements the Industry 4.0 strategy in the production of drugs and uses one or many of the different concepts of horizontal communication, i.e., Cloud Computing [ 44 ], IIoT [ 45 ], Connectivity [ 46 ], Big Data [ 47 ], Digital Twins [ 48 ], ID/Loc [ 46 , 49 ], Semantics [ 50 , 51 ], SOA [ 52 ]. Of course, we can assume other solutions, but the eight mentioned are key concepts of the CPS I4.0.…”

Section: Methodsmentioning

confidence: 99%

Application of Feature Extraction Methods for Chemical Risk Classification in the Pharmaceutical Industry

Topolski

2021

Sensors

View full text Add to dashboard Cite

The features that are used in the classification process are acquired from sensor data on the production site (associated with toxic, physicochemical properties) and also a dataset associated with cybersecurity that may affect the above-mentioned risk. These are large datasets, so it is important to reduce them. The author’s motivation was to develop a method of assessing the dimensionality of features based on correlation measures and the discriminant power of features allowing for a more accurate reduction of their dimensions compared to the classical Kaiser criterion and assessment of scree plot. The method proved to be promising. The results obtained in the experiments demonstrate that the quality of classification after extraction is better than using classical criteria for estimating the number of components and features. Experiments were carried out for various extraction methods, demonstrating that the rotation of factors according to centroids of a class in this classification task gives the best risk assessment of chemical threats. The classification quality increased by about 7% compared to a model where feature extraction was not used and resulted in an improvement of 4% compared to the classical PCA method with the Kaiser criterion, with an evaluation of the scree plot. Furthermore, it has been shown that there is a certain subspace of cybersecurity features, which complemented with the features of the concentration of volatile substances, affects the risk assessment of chemical hazards. The identified cybersecurity factors are the number of packets lost, incorrect Logins, incorrect sensor responses, increased email spam, and excessive traffic in the computer network. To visualize the speed of classification in real-time, simulations were carried out for various systems used in Industry 4.0.

show abstract

“…Another concept commonly found in Industry 4.0 is DT, which is the construction of a realistic computational model (virtual replica) of the monitored device by means of analytical methods and tools [15]. High-fidelity virtual models are usually achieved with Multiphysics numerical simulations, such as the one performed in [16], in which a structural coupling technique was used to model a power transformer under stressful operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin-Based Monitoring System of Induction Motors Using IoT Sensors and Thermo-Magnetic Finite Element Analysis

Santos¹,

Tshoombe²,

Santos³

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Electric induction motors are the type of motor most commonly operated in industry, and for this reason technologies that predict faults and reduce the corrective maintenance are of great interest. In this context, this paper presents a predictive maintenance tool of electric motors using the concepts of Digital Twin (DT) and Industrial Internet of Things (IIoT). The proposed system is innovative, as it monitors the motor current and temperature by means of sensors and a low-cost acquisition module, and these measurements are sent via Wi-Fi to a database. The concept of DT was leveraged by providing the measurements as inputs to a high-fidelity strongly-coupled model of the monitored monitor, using the Finite Element Method (FEM). The results obtained are satisfactory, because the sensors used presented acceptable errors that do not interfere with the reliability of the results. The computer simulation showed relative errors below 4% in the conductivity analysis and 10% in the temperature analysis. In addition, the simulation allows verifying the internal temperature of the motor, its resistive losses, and the intensity of the magnetic flux at each pole. It is worth pointing out that the internal analysis performed is only possible due to the combination of IIoT and computer simulations. Therefore, they allow a better diagnosis of the motor's operational status and also a time estimate for the next maintenance service, thus being ideal for the industrial sector.INDEX TERMS Condition monitoring, digital twin, finite element analysis, induction motors, Internet of Things.

show abstract

Digital twin system design for dual-manipulator cooperation unit

Cited by 32 publications

References 7 publications

Review of Electric Vehicle Testing Procedures for Digital Twin Development: A Comprehensive Analysis

Review of Electric Vehicle Testing Procedures for Digital Twin Development: A Comprehensive Analysis

Application of Feature Extraction Methods for Chemical Risk Classification in the Pharmaceutical Industry

Digital Twin-Based Monitoring System of Induction Motors Using IoT Sensors and Thermo-Magnetic Finite Element Analysis

Contact Info

Product

Resources

About