Identification of Pipeline Leak Sizes Based on Chaos-Grey Wolf-Support Vector Machine
Accurately identifying pipeline leak size is of great significance for hazard assessment and timely rescue. This paper proposes an identification method of non-metallic pipeline leak size based on chaos-grey wolf-support vector machine (C-G-SVM). The acoustic signal features of different leak sizes...
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| Vydané v: | IEEE sensors journal Ročník 23; číslo 19; s. 1 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
IEEE
01.10.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1530-437X, 1558-1748 |
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| Abstract | Accurately identifying pipeline leak size is of great significance for hazard assessment and timely rescue. This paper proposes an identification method of non-metallic pipeline leak size based on chaos-grey wolf-support vector machine (C-G-SVM). The acoustic signal features of different leak sizes are extracted from the perspectives of time domain, frequency domain, and shape. By using the Grey Relational Analysis (GRA) method, the dimensionality of the above features is further reduced. Then, a non-metallic pipeline leak size identification model based on C-G-SVM is established. The parameters of the SVM model are optimized by combining chaotic local search with grey wolf optimization algorithm to improve the identification accuracy of pipeline leak sizes. Finally, the influences of different features, identification methods, and sampling duration on the identification accuracy of pipeline leak sizes are compared and analyzed. The analysis of non-metallic pipeline leak test data based on acoustic methods verifies the effectiveness of this method. When the sampling duration is 20 s, the average identification accuracy reaches over 90%. The results show that this method can accurately identify the leak size of non-metallic pipelines, providing a theoretical basis for engineering applications. |
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| AbstractList | Accurately identifying pipeline leak size is of great significance for hazard assessment and timely rescue. This paper proposes an identification method of non-metallic pipeline leak size based on chaos-grey wolf-support vector machine (C-G-SVM). The acoustic signal features of different leak sizes are extracted from the perspectives of time domain, frequency domain, and shape. By using the Grey Relational Analysis (GRA) method, the dimensionality of the above features is further reduced. Then, a non-metallic pipeline leak size identification model based on C-G-SVM is established. The parameters of the SVM model are optimized by combining chaotic local search with grey wolf optimization algorithm to improve the identification accuracy of pipeline leak sizes. Finally, the influences of different features, identification methods, and sampling duration on the identification accuracy of pipeline leak sizes are compared and analyzed. The analysis of non-metallic pipeline leak test data based on acoustic methods verifies the effectiveness of this method. When the sampling duration is 20 s, the average identification accuracy reaches over 90%. The results show that this method can accurately identify the leak size of non-metallic pipelines, providing a theoretical basis for engineering applications. Accurately identifying pipeline leak size is of great significance for hazard assessment and timely rescue. This article proposes an identification method of nonmetallic pipeline leak size based on chaos-gray wolf-support vector machine (C-G-SVM). The acoustic signal features of different leak sizes are extracted from the perspectives of time domain, frequency domain, and shape. By using the gray relational analysis (GRA) method, the dimensionality of the above features is further reduced. Then, a nonmetallic pipeline leak size identification model based on C-G-SVM is established. The parameters of the SVM model are optimized by combining chaotic local search with gray wolf optimization (GWO) algorithm to improve the identification accuracy of pipeline leak sizes. Finally, the influences of different features, identification methods, and sampling duration on the identification accuracy of pipeline leak sizes are compared and analyzed. The analysis of nonmetallic pipeline leak test data based on acoustic methods verifies the effectiveness of this method. When the sampling duration is 20 s, the average identification accuracy reaches over 90%. The results show that this method can accurately identify the leak size of nonmetallic pipelines, providing a theoretical basis for engineering applications. |
| Author | Gao, Yan Han, Xiaojuan Liu, Junzengjing Cui, Xiwang Yan, Zhaoli |
| Author_xml | – sequence: 1 givenname: Xiaojuan orcidid: 0000-0003-2025-3948 surname: Han fullname: Han, Xiaojuan organization: School of Control and Computer Engineering, North China Electric Power University, Beijing, China – sequence: 2 givenname: Junzengjing surname: Liu fullname: Liu, Junzengjing organization: School of Control and Computer Engineering, North China Electric Power University, Beijing, China – sequence: 3 givenname: Xiwang orcidid: 0000-0001-5053-8317 surname: Cui fullname: Cui, Xiwang organization: School of Instrument Science and Opto-electronics Engineering, Beijing Information Science and Technology University, Beijing, China – sequence: 4 givenname: Yan orcidid: 0000-0002-7253-5309 surname: Gao fullname: Gao, Yan organization: Institute of Acoustics, State Key Laboratory of Acoustics, Chinese Academy of Sciences, Beijing, China – sequence: 5 givenname: Zhaoli surname: Yan fullname: Yan, Zhaoli organization: Institute of Acoustics, Key Laboratory of Noise and Vibration Research, Chinese Academy of Sciences, Beijing, China |
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| SubjectTerms | Accuracy Acoustics Algorithms Chaotic local search Feature extraction Grey relational analysis Grey Wolf optimization Hazard assessment Identification Identification methods Leak detection Optimization Parameter identification Pipelines Sampling Shape Support vector machine Support vector machines Time-domain analysis |
| Title | Identification of Pipeline Leak Sizes Based on Chaos-Grey Wolf-Support Vector Machine |
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