Digital Twin Model Quality Optimization and Control Methods Based on Workflow Management

Luo, Ruiping; Sheng, Buyun; Lu, Yingkang; Huang, Yuzhe; Fu, Gaocai; Yin, Xiyan

doi:10.3390/app13052884

Cited by 5 publications

(13 citation statements)

References 20 publications

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“…4. The black dots indicate the samples in the node [6,10]. This result is confirmed further by the fact that when the shut-off valve is triggered, the system needs some time to migrate to a new operating point.…”

Section: Input Data Confusion Matrixmentioning

confidence: 60%

“…Thus, a more homogeneous output that preserves as much as possible the initial topological composition returns. There are several neighbourhood functions in the literature, such as the Bubble Equation ( 9), Gaussian Equation (10), Cutgass Equation (11), and Epanechikov Equation ( 12) [43].…”

Section: The Self-organizing Mapmentioning

confidence: 99%

“…NumPy and Pandas packages were needed to manipulate the data, while the MiniSom library was used for map creation and the Bokeh package for graphical representation. The MiniSom Function appears as follows: SOM = MiniSom(x, y, input len, topology, sigma, learning rate, neighborhood function, seed) (27) where x and y are the numbers of rows and columns of the output grid, input len is the length of the database supplied to the network, topology defines the topology of the grid (rectangular or hexagonal), sigma is the initial radius of the neighbourhood, learning rate represents the α(0), neighborhood function is Equation (10), and the seed is the seed of the network.…”

Section: The Algorithmmentioning

confidence: 99%

“…In this case, dark-coloured boxes indicate those nodes that are recalled the most during the training phase and vice versa for the light-coloured boxes. The most responsive nodes have coordinates [5,10] and [6,10]. As shown in Figure 10, all readings of the system's steady state are placed in these two locations.…”

Section: Output Mapmentioning

confidence: 99%

“…The Internet of Things (IoT) and Digital Twin (DT) [10] can be considered as the guiding force for this crucial digital transformation, as they enable the real-time gathering, handling, and interpretation of data to accomplish these objectives [11][12][13][14]. The transition from a traditional factory to a smart factory brings a higher level of integration of physical production with digital technologies [15,16] with particular attention to standards such as ISO14224:2016 for oil and gas industries [17].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector

2023

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Anomaly detection plays a crucial role in preserving industrial plant health. Detecting and identifying anomalies helps prevent any production system from damage and failure. In complex systems, such as oil and gas, many components need to be kept operational. Predicting which parts will break down in a time interval or identifying which ones are working under abnormal conditions can significantly increase their reliability. Moreover, it underlines how the use of artificial intelligence is also emerging in the process industry and not only in manufacturing. In particular, the state-of-the-art analysis reveals a growing interest in the subject and that most identified algorithms are based on neural network approaches in their various forms. In this paper, an approach for fault detection and identification was developed using a Self-Organizing Map algorithm, as the results of the obtained map are intuitive and easy to understand. In order to assign each node in the output map a single class that is unique, the purity of each node is examined. The samples are identified and mapped in a two-dimensional space, clustering all readings into six macro-areas: (i) steady-state area, (ii) water anomaly macro-area, (iii) air-water anomaly area, (iv) tank anomaly area, (v) air anomaly macro-area, (vi) and steady-state transition area. Moreover, through the confusion matrix, it is found that the algorithm achieves an overall accuracy of 90 per cent and can classify and recognize the state of the system. The proposed algorithm was tested on an experimental plant at Università Politecnica delle Marche.

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Section: Input Data Confusion Matrixmentioning

confidence: 60%

Section: The Self-organizing Mapmentioning

confidence: 99%

Section: The Algorithmmentioning

confidence: 99%

Section: Output Mapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector

2023

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A Systematic Review of the Digital Transformation of the Building Construction Industry

Naji,

Gunduz,

Alhenzab

et al. 2024

IEEE Access

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Construction sector spending makes a significant contribution to the global economy, with approximately $10 trillion being spent on building and construction activities annually. However, the construction industry has traditionally been perceived as slow to adapt to new technologies compared to other sectors. Recently, the construction industry has experienced a substantial shift towards Digital Transformation. As new technologies have emerged, the construction industry has begun to realize the importance of Digital Transformation in the pre-construction, construction, and facility management phases. A high degree of Digital Transformation has been seen regarding site monitoring, wearables, sensors, and identifying hazards. This paper intends to sketch a global picture of digital technologies implemented in the construction industry throughout the entire project lifecycle. By fully analyzing more than 200 papers, the paper finds that various aspects of the construction industry, including technologies, policies, regulations, and infrastructures, are still in the early stages of Digital Transformation. The findings from this review will help researchers and practitioners in the construction industry understand the global picture of digital technology implementation and where the construction industry stands in the Digital Transformation process. This paper also serves as a starting point for future work on Digital Transformation in the construction industry. The research paper is limited to vertical aspects in building projects and does not include horizontal integration. Finally, this study will give a guideline with successful examples of which technologies are being used in specific phases, so future researchers can get a holistic view of the use of digital technology in the entire building environment.

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Assembly feature construction method of equipment mesh model for digital twin workshops

Luo,

Sheng,

Song

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part B:

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The digital twin workshop is the basic unit for realizing smart manufacturing. Its model is the prerequisite for applying digital twins in workshops. Geometric model assembly is the primary concern in workshop-level digital twin modeling. However, current research lacks sufficient focus on the efficiency and application modes of equipment mesh model assembly features in geometric model assembly, hindering the construction of the workshop-level digital twin model. The equipment-level digital twin model typically only contains assembly information between individual parts and is generally represented by polygonal meshes; the lack of assembly information between equipment models impedes the assembly of the workshop-level digital twin model. To improve the efficiency of workshop-level digital twin modeling and the reusability of the original 3D assembly information, this paper proposes an assembly feature construction method of equipment mesh model for digital twin workshops. A 3D assembly information model is constructed to describe the information related to the equipment geometric model and the assembly features. Then, the assembly features of the equipment mesh model are gradually positioned preliminarily and precisely, achieving precise mapping of the assembly features in the 3D information model to the equipment mesh model and efficient construction of equipment mesh model assembly features. Finally, with the construction and application of equipment mesh model assembly features in an arc welding workshop and storage workshop as an example, the feasibility of the method in improving the modeling efficiency is verified through relevant comparative experiments.

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Digital Twin Model Quality Optimization and Control Methods Based on Workflow Management

Cited by 5 publications

References 20 publications

An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector

An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector

A Systematic Review of the Digital Transformation of the Building Construction Industry

Assembly feature construction method of equipment mesh model for digital twin workshops

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