Machine-Learning-Driven Digital Twin for Lifecycle Management of Complex Equipment

Ren, Zijie; Pan, Deng

doi:10.1109/tetc.2022.3143346

Cited by 63 publications

(21 citation statements)

References 32 publications

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“…In addition, three cases are given to illustrate the future application of DTs body in three stages of the product. More than ever before, manufacturers today need to adapt to changing customer needs, rising resource costs, and increasing uncertainty [52][53][54]. One promising approach to these problems is the digitization of manufacturing systems.…”

Section: Dts Modeling Contributes To Intelligent Manufacturingmentioning

confidence: 99%

Modeling Methods of 3D Model in Digital Twins

Liu¹,

Li²,

Lv³

2023

Computer Modeling in Engineering &Amp; Sciences

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To understand the current application and development of 3D modeling in Digital Twins (DTs), abundant literatures on DTs and 3D modeling are investigated by means of literature review. The transition process from 3D modeling to DTs modeling is analyzed, as well as the current application of DTs modeling in various industries. The application of 3D DTs modeling in the elds of smart manufacturing, smart ecology, smart transportation, and smart buildings in smart cities is analyzed in detail, and the current limitations are summarized. It is found that the 3D modeling technology in DTs has broad prospects for development and has a huge impact on all walks of life and even human lifestyles. At the same time, the development of DTs modeling relies on the development and support capabilities of mature technologies such as Big Data, Internet of Things, Cloud Computing, Arti cial Intelligence, and game technology. Therefore, although some results have been achieved, there are still limitations. This work aims to provide a good theoretical support for the further development of 3D DTs modeling.

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Section: Dts Modeling Contributes To Intelligent Manufacturingmentioning

confidence: 99%

Modeling Methods of 3D Model in Digital Twins

Liu¹,

Li²,

Lv³

2023

Computer Modeling in Engineering &Amp; Sciences

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“…In addition, DL has also achieved excellent research results in the fields of natural language processing, text detection, image processing, speech recognition, remote sensing, medical image recognition, etc. by applying deep learning and processing to information [ 16 , 17 ]. In the following, some outstanding achievements of DL in these fields are presented briefly.…”

Section: Deep Learning (Dl) Algorithms and Applications In Rop Researchmentioning

confidence: 99%

A Deep-Learning-Based Collaborative Edge–Cloud Telemedicine System for Retinopathy of Prematurity

Luo¹,

Ding

Hou

2022

Sensors

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Retinopathy of prematurity is an ophthalmic disease with a very high blindness rate. With its increasing incidence year by year, its timely diagnosis and treatment are of great significance. Due to the lack of timely and effective fundus screening for premature infants in remote areas, leading to an aggravation of the disease and even blindness, in this paper, a deep learning-based collaborative edge-cloud telemedicine system is proposed to mitigate this issue. In the proposed system, deep learning algorithms are mainly used for classification of processed images. Our algorithm is based on ResNet101 and uses undersampling and resampling to improve the data imbalance problem in the field of medical image processing. Artificial intelligence algorithms are combined with a collaborative edge–cloud architecture to implement a comprehensive telemedicine system to realize timely screening and diagnosis of retinopathy of prematurity in remote areas with shortages or a complete lack of expert medical staff. Finally, the algorithm is successfully embedded in a mobile terminal device and deployed through the support of a core hospital of Guangdong Province. The results show that we achieved 75% ACC and 60% AUC. This research is of great significance for the development of telemedicine systems and aims to mitigate the lack of medical resources and their uneven distribution in rural areas.

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“…Simulation of physical process on ad hoc or continuous basis process simulation [63], automated simulation model generation, [64] Digital model richness Robustness, resilience, self-adaption, fidelity of virtual model Robustness, resilience, self-adaption, fidelity [44], DT fidelity [53], fidelity [61], DT behaviour model [37], high-fidelity of DTs [64] Human interaction Bridging human and machine Human-machine collaboration [5], bridges a human user and robot [25] Product life-cycle Product design, manufacturing and service Service stage: service, data analytics [38], Full product life-cycle management [37,63,65], Manufacturing stage: fault prediction [3], predicting energy efficiency [37], predictive maintenance, feature extraction [30] Figure 16 shows contribution of ML-based DT in manufacturing PLM. ML-based DT is marginally used in full product life-cycle management [37,63,65]. Conversely, in 92.68% of cases, ML-based DT is used for the product manufacturing stage.…”

Section: Simulation Capabilitiesmentioning

confidence: 99%

“…Additionally, big data analytics [67] and information weighting [40] appeared as a dominant future research directions in the period 2020-2022. In 2022 and onwards, the incorporation of time-series [65] and categorical data [36], encapsulation of works in progress [68], data heterogeneity [43], real-time data [63], and data quality improvement will be dominant in ML-based DT.…”

Section: Data-based Taskmentioning

confidence: 99%

Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview

2022

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The Digital Twin (DT) concept in the manufacturing industry has received considerable attention from researchers because of its versatile application potential. Machine Learning (ML) adds a new dimension to DT by enhancing its functionality. Many studies on DT in the manufacturing industry have recently been published. However, there is still a lack of a systematic literature review on different aspects of ML-based DT in the manufacturing industry from a bibliometric and evolutionary perspective. Therefore, the proposed study is mainly aimed at reviewing DT in the manufacturing industry to identify the contribution of ML, current methods, and future research directions. According to the findings, the contribution of ML to this domain is significant. Additionally, the results show that the latest ML technologies are being used in the DT domain; neural networks have evolved based on application-specific requirements. The total number of papers and citations per paper on ML-based DT is increasing. The relevance of ML in DT has increased over time. The current trend is to use ML-based DT for data analytics. Additionally, there are many unfilled gaps; certain gaps include industrial applications of DT, synchronisation with real-time data through sensors, heterogeneous data management, and benchmarking.

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Machine-Learning-Driven Digital Twin for Lifecycle Management of Complex Equipment

Cited by 63 publications

References 32 publications

Modeling Methods of 3D Model in Digital Twins

Modeling Methods of 3D Model in Digital Twins

A Deep-Learning-Based Collaborative Edge–Cloud Telemedicine System for Retinopathy of Prematurity

Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview

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