Abstract
The pipelines are used for transporting fluids and it is an important part of the media transportation for oil and gas. However, as pipelines are often spread across vast distances and carry certain hazardous substances, the chances for accidents such as leakage accidents in oil and gas pipelines are increased. Variety of factors lead to pipeline leakage accidents such as corrosion, vibration and other impacts affecting the safe operation of pipelines. Pipelines leakages cause both loss of product and as well as environmental damage. Acoustic emissions sensors have recently emerged as a promising tool for long distance pipeline monitoring due to the acoustic emission sensors advantages of high accuracy and low loss per distance. The signal processing is used to decompose the raw signal and the pre-processed signal will be analyzed in the time-frequency domain. Several existing signals processing methods such as Fourier Transform, Wavelet Transform can be used for extracting useful information. The parameters of Empirical Mode Decomposition [EMD] show promising results. The promising results in terms of accuracy of selections IMFs and analysis of time-frequency domain. The selected of Intrinsic Mode Functions [IMFs] IMFs are analyzed in the time domain by using two parameters which are standard deviation and variance. The selected IMFs are obtained to reveal the leakage and no leakage signatures of the pipeline.
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References
Petro Wiki (2015). https://petrowiki.org/Pipelines
Iannuzzi, M., Barnoush, A., Johnsen, R.: Materials and corrosion trends in offshore and subsea oil and gas production. npj Mater. Degrad. 1(1), 2 (2017)
Rezaei, H., Ryan, B., Stoianov, I.: Pipe failure analysis and impact of dynamic hydraulic conditions in water supply networks. Procedia Eng. 119, 253–262 (2015)
Zardasti, L., Hanafiah, N.M., Noor, N.M., Yahaya, N., Rashid, A.S.A.: The consequence assessment of gas pipeline failure due to corrosion. Solid State Phenom. 227, 225–228 (2015)
Yahaya, N., Noor, N.M., Othman, S.R., Sing, L.K., Din, M.M.: New technique for studying soil-corrosion of underground pipeline. J. Appl. Sci. 11(9), 1510–1518 (2011)
Shama, A.A.M., El-Rashid, A., El-Shaib, M., Kotb, D.M.: Review of leakage detection methods for subsea pipeline
Pipeline leak detection market of the oil and gas industry 2018-2028, 01 May 2018. https://www.visiongain.com/report/pipeline-leak-detection-market-of-the-oil-and-gas-industry-2018-2028/
Signal processing [wikipedia]. https://en.wikipedia.org/wiki/Signal_processing
Time Series [wikipedia]. https://en.wikipedia.org/wiki/Time_series
Spectral density estimation [wikipedia]. https://en.wikipedia.org/wiki/Spectral_density_estimation
Time - frequency analysis [wikipedia]. https://en.wikipedia.org/wiki/Time%E2%80%93frequency_analysis
Adnan, N., Ghazali, M., Amin, M., Hamat, A.: Leak detection in gas pipeline by acoustic and signal processing-A review. In: IOP Conference Series: Materials Science and Engineering, vol. 100, no. 1, p. 012013. IOP Publishing (2015)
Xiao, J., Li, J., Sun, J., Feng, H., Jin, S.: Natural-gas pipeline leak location using variational mode decomposition analysis and cross-time–frequency spectrum. Measurement 124, 163–172 (2018)
Mahmood, A.K.: An approach of filtering to select IMFs of EEMD in signal processing for acoustic emission [AE] sensors. In: Intelligent Systems in Cybernetics and Automation Control Theory, p. 100 (2019)
Park, S., Kim, S., Choi, J.-H.: Gear fault diagnosis using transmission error and ensemble empirical mode decomposition. Mech. Syst. Signal Process. 108, 262–275 (2018)
Lu, C., Ding, P., Chen, Z.: Time-frequency analysis of acoustic emission signals generated by tension damage in CFRP. Procedia Eng. 23, 210–215 (2011)
Li, C.J., Zhang, J.P., Zhang, Z.Q., Hu, J.L., Li, Y.: The research on leak detection technology of natural gas pipeline based on EMD. In: Applied Mechanics and Materials, vol. 494, pp. 793–796. Trans Tech Publications Ltd. (2014)
Ma, Y., Cui, X., Yan, Y., Ma, L., Han, X.: Leakage detection in a CO2 pipeline using Acoustic emission techniques. In: 2014 12th International Conference on Signal Processing (ICSP), pp. 78–82. IEEE (2014)
Hanafi, M.Y., Yusof, M.F.M., Remli, M., Kamarulzaman, M.H.: Pipe leak diagnostic using high frequency piezoelectric pressure sensor and automatic selection of intrinsic mode function (2017)
Mostafapour, A., Davoudi, S.: Analysis of leakage in high pressure pipe using acoustic emission method. Appl. Acoust. 74(3), 335–342 (2013)
Bose, P.A., Sasikumar, T., Jose, P.A., Philip, J.: Acoustic emission signal analysis and event extraction through tuned wavelet packet transform and continuous wavelet transform while tensile testing the AA 2219 coupon. Int. J. Acoust. Vib. 23(2), 234–239 (2018)
Ahadi, M., Bakhtiar, M.S.: Leak detection in water-filled plastic pipes through the application of tuned wavelet transforms to acoustic emission signals. Appl. Acoust. 71(7), 634–639 (2010)
Acknowledgements
This work was supported by the Development of Intelligent Pipeline Integrity Management System (I-PIMS) Grant Scheme from Universiti Teknologi PETRONAS.
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Jaafar, N.S.M., Aziz, I.A., Hasan, M.H.B., Mahmood, A.K. (2019). Parameter Calculation in Time Analysis for the Approach of Filtering to Select IMFs of EMD in AE Sensors for Leakage Signature. In: Silhavy, R. (eds) Artificial Intelligence Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 985. Springer, Cham. https://doi.org/10.1007/978-3-030-19810-7_14
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DOI: https://doi.org/10.1007/978-3-030-19810-7_14
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