1. bookVolume 49 (2019): Issue 4 (December 2019)
Journal Details
License
Format
Journal
eISSN
2083-4608
First Published
26 Feb 2008
Publication timeframe
4 times per year
Languages
English
access type Open Access

Analysis of Possibility of Phase Shift Model Usage for Identification of Gas Leakage from Pipeline with Corrective Elements

Published Online: 31 Dec 2019
Volume & Issue: Volume 49 (2019) - Issue 4 (December 2019)
Page range: 363 - 381
Journal Details
License
Format
Journal
eISSN
2083-4608
First Published
26 Feb 2008
Publication timeframe
4 times per year
Languages
English
Abstract

Proposed method of leakage identification uses a parametric model in the form of signals phase shift (φ). Signals from the test stand will be used: r1, r2 - signals from the corrective elements connected to the tested pipeline, pressure signals x, mass flow y and simulated mass flow yw. For these signals, their auto and cross power spectral densities are determined: Sxx, Syy, Srr, Sxy, Sxr, Syr, which are the basis to determine spectral transfer functions described as the relations between these signals and next the phase shifts (φ). Thanks that, we get a large set of transfer functions, hence a large set of phase shifts between the measured.

Keywords

1. Abdulimen K.E., Susu A.A.: Liquid pipeline leak detection system: model development and numerical simulation. Chemical Engineering Journal, Vol. 97, Iss. 1, 2004.10.1016/S1385-8947(03)00098-6Search in Google Scholar

2. Colombo A.F., Lee P., Karney B.W.: A selective literature review of transient-based leak detection methods. Journal of Hydro-Environment Research, Vol. 2, No. 4, 2009.10.1016/j.jher.2009.02.003Search in Google Scholar

3. Ferrante M., Brunone B.: Pipe system diagnosis and leak detection by unsteady-state tests. 2. Wavelet analysis. Advances in Water Resources, Vol. 26, No. 1, 2003.10.1016/S0309-1708(02)00102-1Search in Google Scholar

4. Ghazali M.F., Beck S.B.M., Shucksmith J.D., Boxall J.B., Staszewski W.J.: Comparative study of instantaneous frequency based methods for leak detection in pipeline networks. Mechanical Systems and Signal Processing, Vol. 29, 2012.10.1016/j.ymssp.2011.10.011Search in Google Scholar

5. Grądzki R., Golak K., Lindstedt P., Bartoszewicz B.: Reasons for the experimental research of gas outflows based on the signals of weak interactions between the tested model of the gas pipeline, and tested equalizer. Journal of KONBiN, Vol. 34, Iss. 1, 2015, DOI 10.1515/jok-2015-0022.10.1515/jok-2015-0022Open DOISearch in Google Scholar

6. Grądzki R., Golak K., Lindstedt P.: Basics of the Corrective - Resonant Spectral Method of Gas Flow Identification from the Damaged Pipeline and its Experimental Verification. Journal of KONBiN, Vol. 44, 2017, DOI 10.1515/jok-2017-0066.10.1515/jok-2017-0066Open DOISearch in Google Scholar

7. Grądzki R., Golak K., Lindstedt P.: Parametric and nonparametric diagnostic models for blades in the rotating machinery with environment elimination. Journal of KONES Vol. 23, nr 2, 2016.10.5604/12314005.1213581Search in Google Scholar

8. Hao J., Zhang L., Wei L., Ding Q.: Integrated leakage detection and localization model for gas pipelines based on the acoustic wave method. Journal of Loss Prevention in the Process Industries, Vol. 27, 2014.10.1016/j.jlp.2013.11.006Search in Google Scholar

9. Kowalczuk Z., Gunawickrama K.: Detekcja i lokalizacja wycieków w rurociągach przesyłowych. In: J. Korbicz, J. Kościelny, Z. Kowalczuk, W. Cholewa (red.): Diagnostyka procesów. Modele metody sztucznej inteligencji, zastosowania. WNT, Warszawa, 2002.Search in Google Scholar

10. Laurentys C.A., Bomfim C.H.M., Menezes B.R., Caminhas W.M.: Design of a pipeline leakage detection using expert system: A novel approach. Journal Applied Soft Computing, Vol. 11, Iss. 1, 2011.10.1016/j.asoc.2010.02.005Search in Google Scholar

11. Lee P.J., Vítkovský J.P., Lambert M.F., Simpson A.R., Liggett J.A.: Frequency domain analysis for detecting pipeline leaks. Journal of Hydraulic Engineering, Vol. 131, No. 7, 2005.10.1061/(ASCE)0733-9429(2005)131:7(596)Search in Google Scholar

12. Li S., Wen Y., Li P., Yang J.: Leak location in gas pipelines using cross-time frequency spectrum of leakage-induced acoustic vibrations. Journal of Sound and Vibration, Vol. 333, No. 17, 2014.10.1016/j.jsv.2014.04.018Search in Google Scholar

13. Lindstedt P., Grądzki R.: Model for blade diagnosis in a working rotor machine employing the method of virtual elimination of stochastic environment. Archive of Mechanical Engineering, Vol. 58, Nr 3, 2011.10.2478/v10180-011-0020-8Search in Google Scholar

14. Lindstedt P.: Słabe interakcje w procesie diagnozowania wycieków z układów hydraulicznych. Prace Naukowe Instytutu Technicznego Wojsk Lotniczych, nr 10, 2000.Search in Google Scholar

15. Lindstedt P.: The method of complex worthiness assessment of an engineering object in the process of its use and service. Solid State Phenomena, Vol. 144/2009, Trans Tech Publications, Switzerland 2009.10.4028/www.scientific.net/SSP.144.45Search in Google Scholar

16. Lindstedt P.: Weak interactions between objects in the signal-based and parametric diagnostics of transport-dedicated complex engineering systems. Aircraft Engineering and Aerospace Technology, Vol. 77, Nr 3, 2005.10.1108/00022660510597241Search in Google Scholar

17. Liu P.T., Gong R.K., Gong Y.H., Wang C.H.: A Gas Pipeline Leakage Diagnosis of Fusing BP Neural Network Basing on WSN and D-S Theory. Applied Mechanics and Materials, V. 541-542, 2014.10.4028/www.scientific.net/AMM.541-542.1442Search in Google Scholar

18. Ostapkowicz P.: Signals of weak interobject interactions in diagnosing of lekages from pipelines. Eksploatacja i niezawodność, nr 33, 1/2007.Search in Google Scholar

19. Rajtar J.M., Muthiah R.: Pipeline leak detection system for oil and gas flowlines. Journal of Manufacturing Science and Engineering, Transactions of the ASME, Vol. 119, no. 1, 1997.10.1115/1.2836545Search in Google Scholar

20. Rashid S., Qaisar S., Saeed H., Felemban E.: Performance analysis of leak detection algorithm in long range pipeline networks using transform analysis. Proceedings of the IEEE Conference on Systems, Process and Control (ICSPC ‘13).Search in Google Scholar

21. Saeed H., Ali S., Rashid S., Qaisar S., Felemban E.: Reliable monitoring of oil and gas pipelines using wireless sensor network (WSN), Proceedings of the IEEE 9th International System of Systems Engineering Conference, 2014.10.1109/SYSOSE.2014.6892493Search in Google Scholar

22. Scott S.L., Barrufet M.A.: Worldwide Assessment of Industry Leak Detection Capabilities for Single & Multiphase Pipelines. Project Report Prepared for the Minerals Management Service, OTRC Library Number: 8/03A120, University of Texas, Austin, 2003.Search in Google Scholar

23. Sivathanu Y.: Natural Gas Leak Detection in Pipelines. U.S. Department of Energy, National Energy Technology Laboratory, 2003.Search in Google Scholar

24. Sobczak R.: Lokalizacja nieszczelności w rurociągach metodą śledzenia czół fal ciśnienia, Przemysł Chemiczny, nr 83, 6/2004.Search in Google Scholar

25. Yang Z., Liu M., Shao M., Ji Y.: Research on Leakage Detection and Analysis of Leakage Point in the Gas Pipeline System, Open Journal of Safety Science and Technology Vol. 1, No. 3, Pub. Date: December 30, 2011.Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo