Leak Detection in Pipelines Using Wavelet Transform and Cepstrum Analysis Methods

Priyandoko Gigih; Mohd Fairusham  Ghazali

doi:10.31258/ijeepse.4.2.159-162

Priyandoko Gigih University of Widyagama, Indonesia
Mohd Fairusham Ghazali Universiti Malaysia Pahang, Malaysia

DOI: https://doi.org/10.31258/ijeepse.4.2.159-162

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Keywords: leak detection, MDPE, wavelet transform, cepstrum analysis

Abstract

Nowadays, a piping system is one of the important features in either home or industrial user. Leak in piping systems is a major operational problem around the world. Leaks result to loss in the fluid through the flow and automatically affect to the economy of the user. Objective of this research is utilizing the signal processing using Wavelet Transform and Cepstrum Analysis methods to leak detect in pipeline. After experiment has been completed, the data analysis process by using Matlab Software takes place. The result shows that the accuracy of the leak location detection is accurate with small error results below 10%.

References

[1] N. F. Adnan, M. F. Ghazali, M. Amin, A. Malik, and A. Ariffin, “Leak detection in MDPE gas pipeline using dual-tree complex wavelet transform”, Australian Journal of Basic and Applied Sciences, vol. 8, no. 15, pp. 356–360, 2014.

[2] N. Adnan, M. Ghazali, M. Amin, and A. Hamat, “Leak detection in gas pipeline by acoustic and signal processing-A review”, vol. 100, no. 1, 2015.

[3] T. M. El-Shiekh, “Leak Detection Methods in Transmission Pipelines,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 32, no. 8, pp. 715–726, Feb. 2010.

[4] F. Wang, W. Lin, Z. Liu, S. Wu, and X. Qiu, “Pipeline leak detection by using time-domain statistical features”, IEEE Sensors Journal, vol. 17, no. 19, pp. 6431–6442, 2017.

[5] L. Boaz, S. Kaijage, and R. Sinde, “An overview of pipeline leak detection and location systems,” pp. 133–137, 2014.

[6] R. Xiao, Q. Hu, and J. Li, “Leak detection of gas pipelines using acoustic signals based on wavelet transform and Support Vector Machine”, Measurement, vol. 146, pp. 479–489, 2019.

[7] M. Zadkarami, M. Shahbazian, and K. Salahshoor, “Pipeline leak diagnosis based on wavelet and statistical features using Dempster–Shafer classifier fusion technique”, Process safety and environmental protection, vol. 105, pp. 156–163, 2017.

[8] L. L. Ting, J. Y. Tey, A. C. Tan, Y. J. King, and F. Abd Rahman, “Water leak location based on improved dual-tree complex wavelet transform with soft thresholding de-noising,” Applied Acoustics, vol. 174, p. 107751, 2021.

[9] H. M. Yusop, M. Ghazali, M. M. Yusof, and W. W. Hamat, “Improvement of Cepstrum Analysis for the Purpose to Detect Leak, Feature and Its Location in Water Distribution System based on Pressure Transient Analysis” Journal of Mechanical Engineering (JMechE), no. 4, pp. 103–122, 2019.

[10] N. Motazedi and S. Beck, “Leak detection using cepstrum of cross-correlation of transient pressure wave signals”, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 232, no. 15, pp. 2723–2735, 2018.

[11] M. F. Lambert, S. T. Nguyen, J. Gong, A. R. Simpson, and A. C. Zecchin, “Leak detection using pseudo random binary sequence excitation and cepstrum analysis”, 2017.

[12] M. Kothandaraman, Z. Law, M. A. Ezra, and C. H. Pua, “Adaptive Independent Component Analysis–Based Cross-Correlation Techniques along with Empirical Mode Decomposition for Water Pipeline Leakage Localization Utilizing Acousto-Optic Sensors”, Journal of Pipeline Systems Engineering and Practice, vol. 11, no. 3, 2020.

[13] L. Chen, J. Li, Y. Zeng, Y. Chen, and W. Liang, “Magnetic Flux Leakage Image Enhancement using Bidimensional Empirical Mode Decomposition with Wavelet Transform Method in Oil Pipeline Nondestructive Evaluation”, Journal of Magnetics, vol. 24, no. 3, pp. 423–428, 2019.

[14] C. Xu, S. Du, P. Gong, Z. Li, G. Chen, and G. Song, “An improved method for pipeline leakage localization with a single sensor based on modal acoustic emission and empirical mode decomposition with Hilbert transform”, IEEE Sensors Journal, vol. 20, no. 10, pp. 5480–5491, 2020.

[15] M. Shi, H. Zhao, Z. Huang, and Q. Liu, “Signal extraction using complementary ensemble empirical mode in pipeline magnetic flux leakage nondestructive evaluation”, Review of Scientific Instruments, vol. 90, no. 7, 2019.