Research on gas pipeline leakage model identification driven by digital twin

• Published in: Systems science & control engineering Vol. 11; no. 1
• Main Authors: Wang, Dongmei, Shi, Shaoxiong, Lu, Jingyi, Hu, Zhongrui, Chen, Jing
• Format: Journal Article
• Language: English
• Published: Macclesfield Taylor & Francis 31.12.2023; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: condition recognition; digital twin; Digital twins; Economic impact; Finite element method; Gas pipelines; Leak detection; Leakage; Mathematical models; Natural gas; Pipeline leakage; Rescue operations; Spatial data; support vector machine; Support vector machines
• ISSN: 2164-2583; 2164-2583
• DOI: 10.1080/21642583.2023.2180687

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.