Research on gas pipeline leakage model identification driven by digital twin
When the gas pipeline leaks, it causes huge economic losses. This paper establishes a digital twin model of a pipeline based on the pressure signal generated by a pipeline leak and researches on pipeline leak detection. First, an online updating of the twin model is established to update the data of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | Systems Science & Control Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2023.2180687 |
| _version_ | 1797447486276632576 |
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| author | Dongmei Wang Shaoxiong Shi Jingyi Lu Zhongrui Hu Jing Chen |
| author_facet | Dongmei Wang Shaoxiong Shi Jingyi Lu Zhongrui Hu Jing Chen |
| author_sort | Dongmei Wang |
| collection | DOAJ |
| description | When the gas pipeline leaks, it causes huge economic losses. This paper establishes a digital twin model of a pipeline based on the pressure signal generated by a pipeline leak and researches on pipeline leak detection. First, an online updating of the twin model is established to update the data of the physical information space and the parameters of the twin model online. Second, a visual model is established to display the spatial data of physical information of pipelines and output data of the digital twin of pipelines in real-time. If pipeline leakage is identified, an alarm would be triggered and a corresponding emergency rescue plan would be initiated based on the the leakage. Finally, the pipeline leakage identification model can be established by analysing the finite element model of the pipeline, and the sample data were obtained and preprocessed to extract the feature vectors. The training model of the Support vector machine (SVM) was used to classify the working conditions. Theoretical analysis and experimental results show that the method proposed in this paper has high detection accuracy, so it is feasible to judge gas pipeline leakage by using digital twin prediction. |
| first_indexed | 2024-03-09T13:56:42Z |
| format | Article |
| id | doaj.art-08bec7d8785e406bb8b493a1e047cb63 |
| institution | Directory Open Access Journal |
| issn | 2164-2583 |
| language | English |
| last_indexed | 2024-03-09T13:56:42Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj.art-08bec7d8785e406bb8b493a1e047cb632023-11-30T12:45:31ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832023-12-0111110.1080/21642583.2023.2180687Research on gas pipeline leakage model identification driven by digital twinDongmei Wang0Shaoxiong Shi1Jingyi Lu2Zhongrui Hu3Jing Chen4SANYA Offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya, People’s Republic of ChinaSANYA Offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya, People’s Republic of ChinaSANYA Offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya, People’s Republic of ChinaSANYA Offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya, People’s Republic of ChinaSANYA Offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya, People’s Republic of ChinaWhen the gas pipeline leaks, it causes huge economic losses. This paper establishes a digital twin model of a pipeline based on the pressure signal generated by a pipeline leak and researches on pipeline leak detection. First, an online updating of the twin model is established to update the data of the physical information space and the parameters of the twin model online. Second, a visual model is established to display the spatial data of physical information of pipelines and output data of the digital twin of pipelines in real-time. If pipeline leakage is identified, an alarm would be triggered and a corresponding emergency rescue plan would be initiated based on the the leakage. Finally, the pipeline leakage identification model can be established by analysing the finite element model of the pipeline, and the sample data were obtained and preprocessed to extract the feature vectors. The training model of the Support vector machine (SVM) was used to classify the working conditions. Theoretical analysis and experimental results show that the method proposed in this paper has high detection accuracy, so it is feasible to judge gas pipeline leakage by using digital twin prediction.https://www.tandfonline.com/doi/10.1080/21642583.2023.2180687Pipeline leakagedigital twinsupport vector machinecondition recognition |
| spellingShingle | Dongmei Wang Shaoxiong Shi Jingyi Lu Zhongrui Hu Jing Chen Research on gas pipeline leakage model identification driven by digital twin Systems Science & Control Engineering Pipeline leakage digital twin support vector machine condition recognition |
| title | Research on gas pipeline leakage model identification driven by digital twin |
| title_full | Research on gas pipeline leakage model identification driven by digital twin |
| title_fullStr | Research on gas pipeline leakage model identification driven by digital twin |
| title_full_unstemmed | Research on gas pipeline leakage model identification driven by digital twin |
| title_short | Research on gas pipeline leakage model identification driven by digital twin |
| title_sort | research on gas pipeline leakage model identification driven by digital twin |
| topic | Pipeline leakage digital twin support vector machine condition recognition |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2023.2180687 |
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