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
Online-Zugang:	Volltext
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