Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review

Nguyen, T L T; Duong, Quang; Nguyen, Truong Van; You, Zhu; Zhou, Li

doi:10.1016/j.ijpe.2021.108381

Cited by 63 publications

(11 citation statements)

References 170 publications

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“…Another option is to prepare a mathematical model of the diagnosed system and analyze the residual data between the model and the system. Today, the investigated system model is called a digital twin [ 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ]. Modeling is a very wide area in which static behavior [ 65 , 66 , 67 ], dynamic changes and continuous system [ 68 , 69 ], and discrete states [ 70 ] of systems can be modeled.…”

Section: General Structure Of Fault Diagnosis and Perspective Mainten...mentioning

confidence: 99%

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity

Łuczak

Brock

Siembab

2023

Sensors

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The human-centric and resilient European industry called Industry 5.0 requires a long lifetime of machines to reduce electronic waste. The appropriate way to handle this problem is to apply a diagnostic system capable of remotely detecting, isolating, and identifying faults. The authors present usage of HTTP/1.1 protocol for batch processing as a fault diagnosis server. Data are sent by microcontroller HTTP client in JSON format to the diagnosis server. Moreover, the MQTT protocol was used for stream (micro batch) processing from microcontroller client to two fault diagnosis clients. The first fault diagnosis MQTT client uses only frequency data for evaluation. The authors’ enhancement to standard fast Fourier transform (FFT) was their usage of sliding discrete Fourier transform (rSDFT, mSDFT, gSDFT, and oSDFT) which allows recursively updating the spectrum based on a new sample in the time domain and previous results in the frequency domain. This approach allows to reduce the computational cost. The second approach of the MQTT client for fault diagnosis uses short-time Fourier transform (STFT) to transform IMU 6 DOF sensor data into six spectrograms that are combined into an RGB image. All three-axis accelerometer and three-axis gyroscope data are used to obtain a time-frequency RGB image. The diagnosis of the machine is performed by a trained convolutional neural network suitable for RGB image recognition. Prediction result is returned as a JSON object with predicted state and probability of each state. For HTTP, the fault diagnosis result is sent in response, and for MQTT, it is send to prediction topic. Both protocols and both proposed approaches are suitable for fault diagnosis based on the mechanical vibration of the rotary machine and were tested in demonstration.

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Section: General Structure Of Fault Diagnosis and Perspective Mainten...mentioning

confidence: 99%

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity

Łuczak

Brock

Siembab

2023

Sensors

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“…These data are then combined with context and are processed before being used to comprehend, analyze, operate and optimize processes within an intelligent building. Nguyen et al, (2022) [84] proposed that the evolution of DT cities began with the 3D type in the 1.0 stage, progressing to the perceptive type in the 2.0 stage and then to the foresight type in the 3.0 stage. With respect to the model, data and calculations, the 1.0 stage 3D model is a spatial static model + statistical data + off-line calculation.…”

Section: Adoption Of Digital Twins In Smart Citiesmentioning

confidence: 99%

Digital Twins for Intelligent Green Buildings

Yang

Wang

2022

Buildings

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At present, the integration of green building, the intelligent building industry and high-quality development are facing a series of new opportunities and challenges. This review aims to analyze the digital development of smart green buildings to make it easier to create contiguous ecological development areas in green ecological cities. It sorts out the main contents of Intelligent Green Buildings (IGB) and summarizes the application and role of Digital Twins (DTs) in intelligent buildings. Firstly, the basic connotations and development direction of IGB are deeply discussed, and the current realization and applications of IGB are analyzed. Then, the advantages of DTs are further investigated in the context of IGB for DT smart cities. Finally, the development trends and challenges of IGB are analyzed. After a review and research, it is found that the realization and application of IGB have been implemented, but the application of DTs remains not quite integrated into the design of IGB. Therefore, a forward-looking design is required when designing the IGBs, such as prioritizing sustainable development, people’s livelihoods and green structures. At the same time, an IGB can only show its significance after the basic process of building the application layer is performed correctly. Therefore, this review contributes to the proper integration of IGB and urban development strategies, which are crucial to encouraging the long-term development of cities, thus providing a theoretical basis and practical experience for promoting the development of smart cities.

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“…In contemporary research, SLR using sophisticated BA tools (Subramanyam, 1983;Van Raan, 2003;Gagolewski, 2011;Aria and Cuccurullo, 2017) has gained significant attention. Recently, some research studies conducting BA in supply chain related areas have appeared in the literature (Fu et al, 2010;Fahimnia et al, 2015;Mishra et al, 2017Mishra et al, , 2018Raza, 2020;Goodell et al, 2021;Nguyen et al, 2022). Limited SLR studies on the topic of ML in supply chains have been conducted and these may not exploit the contemporary tools of BA for identifying research themes (Waller and Fawcett, 2013;Toorajipour et al, 2021).…”

Section: Systematicmentioning

confidence: 99%

“…, 2015; Mishra et al ., 2017, 2018; Raza, 2020; Goodell et al. , 2021; Nguyen et al ., 2022). Limited SLR studies on the topic of ML in supply chains have been conducted and these may not exploit the contemporary tools of BA for identifying research themes (Waller and Fawcett, 2013; Toorajipour et al ., 2021).…”

Section: Introductionmentioning

confidence: 99%

Research themes in machine learning applications in supply chain management using bibliometric analysis tools

Raza

Govindaluri

Bhutta

2022

BIJ

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PurposeThis paper conducts a Systematic Literature Review (SLR) of Machine Learning (ML) in Supply Chain Management through bibliometric and network analysis, the authors are able to grasp key features of the contemporary literature. The study makes use of state-of-the-art analytical framework based on a unified approach to reveal insights from the present body of knowledge and the potentials for future research developments.Design/methodology/approachUnlike standard literature reviews, in SLR, a structured approach is followed. The approach enables utilizing contemporary tools and software packages such as R-package “bibliometrix” and Gephi for exploratory and visual analytics. A number of clustering methods are employed to form clusters. Later, multivariate analysis methodologies are adopted to determine the dominant clusters for the influential co-cited references.FindingsUsing contemporary tools from Bibliometric Analysis (BA), the authors identify in an exploratory analysis, the influential authors, sources, regions, affiliations and papers. In addition, the use of network analysis tools reveals research clusters, topological analysis, key research topics, interrelation and authors’ collaboration along with their patterns. Finally, the optimum number of clusters computed for cluster analysis is decided using a systematic procedure based on multivariate analysis such as k-means and factor analysis.Originality/valueModern-day supply chains increasingly depend on developing superior insights from large amounts of data available from diverse sources in unstructured and semi-structured formats. In order to maintain a competitive edge, the supply chains need to perform speedy analysis of big data using efficient tools that provide real-time decision-making insights. Such an analysis necessitates automated processing using intelligent ML algorithms. Through a BA followed by a detailed data visualization in a network analysis enabled grasping key features of the contemporary literature. The analysis is based on 155 documents from the period 2008 to 2018 selected using a systematic selection procedure.

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Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review

Cited by 63 publications

References 170 publications

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity

Digital Twins for Intelligent Green Buildings

Research themes in machine learning applications in supply chain management using bibliometric analysis tools

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