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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Veröffentlicht in:IEEE sensors journal Jg. 23; H. 19; S. 1
Hauptverfasser: Han, Xiaojuan, Liu, Junzengjing, Cui, Xiwang, Gao, Yan, Yan, Zhaoli
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.10.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
ISSN:1530-437X, 1558-1748
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2023.3307673