Urban hazardous chemicals pipeline leakage positioning method based on CELMD-MCKD

ABSTRACT

Aiming at the difficulty of leak detection of urban hazardous chemical pipelines, this paper proposes a method for locating pipeline leaks based on the complementary ensemble local mean decomposition (CELMD) and maximum correlation kurtosis deconvolution (MCKD) secondary noise reduction. First, white noise with opposite sign was added to the original leak signal in pairs, and the noisy signal was decomposed to obtain a series of product functions (PF). Second, select the PF component containing the main leakage information according to the correlation coefficient, and perform the initial noise reduction. Then, the maximum correlation kurtosis deconvolution (MCKD) was used to perform secondary noise reduction on the filtered PF component; Finally, the PF component obtained after two screening was reconstructed, and the pipeline leakage location was completed by calculating the delay parameters of AE signal by cross-correlation. The experimental results show that compared with the cross-correlation method and the ELMD method, the method has higher recognition accuracy and positioning accuracy.

KEYWORDS:

• Urban pipeline
• complete ensemble local mean decomposition
• correlation coefficient
• maximum correlated kurtosis deconvolution
• leakage location

Acknowledgements

The authors gratefully acknowledge the support provides for a grant from the Research on Key Basic Issues of Risk Prevention and Control of Major Accidents during the Storage and Transportation of Flammable and Explosive Dangerous Chemicals (51834007), Jiangsu Provincial Key Research and Development Program (BE2018642), the Jiangsu Provincial Graduate Research Innovation Project (KYCX18_2622).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Key R&D Program of Jiangsu Province [BE2018642];and National Natural Science Foundation of China (51834007)Postgraduate Research & Practice Innovation Program of Jiangsu Province[KYCX18_2622].