Pipeline Leak Detection and Location Based on Model-Free Isolation of Abnormal Acoustic Signals

Fang Wang, Weiguo Lin, Zheng Liu and Xianbo Qiu
Additional contact information
Fang Wang: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Weiguo Lin: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Zheng Liu: Faculty of Applied Science, University of British Columbia Okanagan, Kelowna, BC V1V 1V7, Canada
Xianbo Qiu: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

Energies, 2019, vol. 12, issue 16, 1-18

Abstract: Pipeline leaks will lead to energy waste, environmental pollution and a threat to human safety. This paper proposes a pipeline leak detection and location method based on the model-free isolation of abnormal (leak and operation) signals. An acoustic signal is first decomposed into “sub-signals” according to its zero-crossing points. Then, based on the definition of signal-to-noise ratio (SNR), the function between the SNR of sub-signal and the number of abnormal sub-signals is established, following which the position of each abnormal sub-signal in the acoustic signal is obtained by tracing its index. Based on this and the cross-correlation analysis, the operation sub-signals can be filtered, which is helpful for the precise leak location. The experimental results demonstrate the computational efficiency and lower false/missing alarm rate of the proposed method that provides an innovative solution for pipeline leak detection.

Keywords: leak detection; signal decomposition; signal-to-noise ratio calculation; model-free; abnormal signal isolation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/16/3172/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/16/3172/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:16:p:3172-:d:258749

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().