Leak Localization of Gas Pipeline Based on the Combination of EEMD and Cross-Spectrum Analysis

Pipeline transportation systems play an increasingly essential role in the development of the economy. An accurate location of leakage is important to reduce economic losses and maintain the safe operation of pipelines. In this article, a pipeline leakage detection method based on infrasound and neg...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2022, Vol.71, p.1-9
Main Authors: Meng, Qiang, Lang, Xianming, Lin, Mingqi, Cai, Zaihong, Zheng, Hao, Song, Huadong, Liu, Wei
Format: Article
Language:English
Subjects:
Correlation analysis
Cross-spectrum analysis
Cutoff frequency
Economic impact
Economics
Elastic waves
Empirical analysis
ensemble empirical mode decomposition (EEMD)
False alarms
Frequencies
Gas pipelines
Infrasound
intrinsic mode function (IMF)
Leak detection
leakage detection
Natural gas
natural gas pipeline
Noise reduction
Pipelines
Signal denoising
Signal processing
Sound waves
Spectrum analysis
Transportation
Transportation systems
Wavelet transforms
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Summary:Pipeline transportation systems play an increasingly essential role in the development of the economy. An accurate location of leakage is important to reduce economic losses and maintain the safe operation of pipelines. In this article, a pipeline leakage detection method based on infrasound and negative pressure waves (NPWs) is adopted. This method can locate the leakage only by collecting the infrasound and pressure signals at the first station of the pipeline, which effectively solves the disadvantages of low positioning accuracy of a signal and easily produces false alarms. In this article, the signal denoising method of cross-spectral analysis combined with ensemble empirical mode decomposition (EEMD) was adopted. First, the leakage signals of sound waves and NPWs are processed by EEMD to obtain a series of intrinsic mode functions (IMFs). The characteristic frequency band is obtained by cross-spectral analysis of the leakage signal. Within this frequency band, the dominant component of the leakage source signal is divided by correlation analysis, and the IMF component is reconstructed to complete the signal denoising. When compared to the single signal pipeline leak detection method, the positioning accuracy of this article showed a 1-3 m improvement on average.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2021.3130680