Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks
Open accessAbstract
In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point’s position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.
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Previous and ongoing work has used sensor and/or satellite data to proactively suggest pipeline maintenance or detect existing leaks.
“Previous and ongoing work has used sensor and/or satellite data to proactively suggest pipeline maintenance [213,898] or detect existing leaks [81,749,823,826]”
From Tackling climate change with machine learning (2023) · cites this paper as [823]
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- Authors
- Wan, J., Yu, Y., Wu, Y., Feng, R., & Yu, N.
- Affiliations
- Beihang University (CN)
- Corresponding author
- Yu, Yang
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2011-12-27
- Volume
- 12
- Issue
- 1
- Pages
- 189-214
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC3279208
- OpenAlex
- W1968398927
- Cited by
- 122
- References
- 40
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 4 · 2023: 12 · 2022: 12 · 2021: 20 · 2020: 19 · 2019: 7 · 2018: 13 · 2017: 9 · 2016: 4 · 2015: 3 · 2014: 5 · 2013: 4 · 2012: 1
- FWCI
- 9.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61001138, 60873240)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Water Systems and Optimization
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Physical Sciences › Engineering › Mechanical Engineering › Structural Integrity and Reliability Analysis
- Keywords
- leak localization, sensor networks, wavelet transform, support vector machine, classifier combination, evidence combination rule, time difference of arrival, false alarm reduction
- MeSH terms
- Algorithms, Computer Communication Networks, Computer Simulation, Decision Making, Environmental Monitoring, Signal Processing, Computer-Assisted, Natural Gas, Support Vector Machine
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