A Novel Broad Learning System Based Leakage Detection and Universal Localization Method for Pipeline Networks

The security of pipeline systems draws attention increasingly; therefore, a novel method based on neural network and graph theory is proposed for the detection and localization of pipeline networks in this paper. First, the detection algorithm based on the broad learning system (BLS) is used to dist...

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Published in:	IEEE access Vol. 7; pp. 42343 - 42353
Main Authors:	Ma, Dazhong, Wang, Junda, Sun, Qiuye, Hu, Xuguang
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptive algorithms adaptive mutation particle swarm algorithm (APSO) broad learning system (BLS) Feature extraction Finite element method generalized cross correlation (GCC) Graph theory Learning systems Localization method Machine learning Manuals Mutation Neural networks Oils Performance enhancement Pipeline leakage detection and localization pipeline networks Pipelines Sensors Vibrations
ISSN:	2169-3536, 2169-3536
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Abstract	The security of pipeline systems draws attention increasingly; therefore, a novel method based on neural network and graph theory is proposed for the detection and localization of pipeline networks in this paper. First, the detection algorithm based on the broad learning system (BLS) is used to distinguish abnormities under large-scale pipeline network environments. During the process, the varied BLS models result in indeterminate performance and fast ergodic structure search is executed via adaptive mutation particle swarm algorithm (APSO) to generate an appropriate structure, succinct parameters, speedability, and accuracy. And manual features are implanted into the BLS feature layer to targetedly improve performance for complex pipeline network signals. Second, based on the detection results, a universal Dijkstra-based applicable localization method is proposed for diverse topological pipeline structures, including mesh-form networks, which have fewer sensors than anchors. The synchronous approximation is adopted to shun local minimum, and the shrinkage of search domain economizes time. Revised BLS was contrasted with several networks trained by real pipeline data and the system was integrated into SCADA and applied on an operational large-scale pipeline network successfully.
AbstractList	The security of pipeline systems draws attention increasingly; therefore, a novel method based on neural network and graph theory is proposed for the detection and localization of pipeline networks in this paper. First, the detection algorithm based on the broad learning system (BLS) is used to distinguish abnormities under large-scale pipeline network environments. During the process, the varied BLS models result in indeterminate performance and fast ergodic structure search is executed via adaptive mutation particle swarm algorithm (APSO) to generate an appropriate structure, succinct parameters, speedability, and accuracy. And manual features are implanted into the BLS feature layer to targetedly improve performance for complex pipeline network signals. Second, based on the detection results, a universal Dijkstra-based applicable localization method is proposed for diverse topological pipeline structures, including mesh-form networks, which have fewer sensors than anchors. The synchronous approximation is adopted to shun local minimum, and the shrinkage of search domain economizes time. Revised BLS was contrasted with several networks trained by real pipeline data and the system was integrated into SCADA and applied on an operational large-scale pipeline network successfully.
Author	Ma, Dazhong Sun, Qiuye Wang, Junda Hu, Xuguang
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SubjectTerms	Adaptive algorithms adaptive mutation particle swarm algorithm (APSO) broad learning system (BLS) Feature extraction Finite element method generalized cross correlation (GCC) Graph theory Learning systems Localization method Machine learning Manuals Mutation Neural networks Oils Performance enhancement Pipeline leakage detection and localization pipeline networks Pipelines Sensors Vibrations
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Title	A Novel Broad Learning System Based Leakage Detection and Universal Localization Method for Pipeline Networks
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