Novel Signal Denoising Approach for Acoustic Leak Detection
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 9, Issue 4
Abstract
If noise easily interferes with acoustic pipeline leak detection technology, false alarms and missing alarms result. This paper proposes a denoising method for the acoustic leak signal sensed by a dynamic pressure transducer (DPT), which is a kind of widely used acoustic sensor. Compared with the normal signal, the leak signal detected by the DPT has stronger energy in low-frequency components. Based on this observation, a numerical integrator was added to the wavelet denoising method for acoustic signal denoising. The numerical integrator can enhance the low-frequency component of a signal. When used for denoising, the wavelet low-pass filter can remove the high-frequency noise first, and then the numerical integrator can enhance the low-frequency leak signal. Therefore, the signal-to-noise ratio (SNR) of the leak signal can be improved. Based on the analysis of the amplitude frequency response of several common numerical integrators, the trapezoidal integrator was selected, which is easy to achieve in engineering. The field experiments show that the method can help reduce the sensitivity of the signal preprocessing result to key parameters of the denoising method including the wavelet basis function and the decomposition scale. Therefore, the stability of signal denoising result can be increased and the robustness of the pipeline leak detection system can be improved.
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Acknowledgments
The authors gratefully acknowledge support from the State Key Laboratory of NBC Protection for Civilians (SKLNBC2014-10), the National Natural Science Foundation of China (61403017), and the Fundamental Research Funds for the Central Universities (XS1702).
References
Almeida, F., M. Brennan, P. Joseph, S. Whitfield, S. Dray, and A. Paschoalini. 2014. “On the acoustic filtering of the pipe and sensor in a buried plastic water pipe and its effect on leak detection: An experimental investigation.” Sensors 14 (3): 5595–5610. https://doi.org/10.3390/s140305595.
Droubi, M. G., N H. Faisal, F. Orr, J. A. Steel, and M. El-Sahib. 2017. “Acoustic emission method for defect detection and identification in carbon steel welded joints.” J. Constr. Steel Res. 134: 28–37. https://doi.org/10.1016/j.jcsr.2017.03.012.
Gao, Y., M. J. Brennan, P. F. Joseph, J. M. Muggletona, and O. Hunaidi. 2004. “A model of the correlation function of leak noise in buried plastic pipes.” J. Sound Vib. 277 (1–2): 133–148. https://doi.org/10.1016/j.jsv.2003.08.045.
Guo, X. L., K. L. Yang, and Y. X. Guo. 2008. “Hydraulic pressure signal denoising using threshold self-learning wavelet algorithm.” J. Hydrodyn. 20 (4): 433–439. https://doi.org/10.1016/S1001-6058(08)60077-3.
Hamilton, M. F., and D. T. Blackstock. 1997. Nonlinear acoustics. San Diego: Academic Press.
Hu, J. Q., L. B. Zhang, and W. Liang. 2011. “Detection of small leakage from long transportation pipeline with complex noise.” J. Loss Prev. Process Ind. 24 (4): 449–457. https://doi.org/10.1016/j.jlp.2011.04.003.
Hunaidi, O., and W. T. Chu. 1999. “Acoustical characteristics of leak signals in plastic water distribution pipes.” Appl. Acoust. 58 (3): 235–254. https://doi.org/10.1016/S0003-682X(99)00013-4.
Kim, M. S., and S. K. Lee. 2009. “Detection of leak acoustic signal in buried gas pipe based on the time-frequency analysis.” J. Loss Prev. Process Ind. 22 (6): 990–994. https://doi.org/10.1016/j.jlp.2008.08.009.
Lin, W. G., and Z. S. Zheng. 2006. “Research on pipeline leak detection based on dynamic pressure signal.” [In Chinese.] Chin. J. Sci. Instrum. 27 (8): 907–910. https://doi.org/10.1109/ICEET.2009.587.
Liou, J. C. P. 1996. “Leak detection by mass balance effective for Norman Wells line.” Oil Gas J. 94 (17): 69–74.
Liu, C. W., Y. X. Li, J. T. Fu, and G. X. Liu. 2015. “Experimental study on acoustic propagation characteristics based leak location method for natural gas pipelines.” Process Saf. Environ. Prot. 96: 43–60. https://doi.org/10.1016/j.psep.2015.04.005.
Loth, J. L., G. J. Morris, G. M. Palmer, R. Guiler, and D. Mehra. 2003. Technology assessment of on-line acoustic monitoring for leaks/infringements in underground natural gas transmission lines. Morgantown, WV: West Virginia Univ.
Mallat, S., and W. L. Hwang. 1991. “Singularity detection and processing with wavelets.” IEEE Trans. Inf. Theory 38 (2): 617–643. https://doi.org/10.1109/18.119727.
Mostafapour, A., and S. Davoodi. 2015. “Continuous leakage location in noisy environment using modal and wavelet analysis with one AE sensor.” Ultrasonics 62: 305–311. https://doi.org/10.1016/j.ultras.2015.06.004.
Muggleton, J. M., M. J. Brennan, and R. J. Pinnington. 2002. “Wavenumber prediction of waves in buried pipes for water leak detection.” J. Sound Vib. 249 (5): 939–954. https://doi.org/10.1006/jsvi.2001.3881.
Murvay, P. S., and L. Silea. 2012. “A survey on gas leak detection and localization techniques.” J. Loss Prev. Process Ind. 25 (6): 966–973. https://doi.org/10.1016/j.jlp.2012.05.010.
Pedersen, G., R. Hauge, and R. Kubilius. 2014. “Active acoustic detection of subsea oil and gas leaks model prediction and measurements.” J. Acoust. Soc. Am. 135 (4): 2303. https://doi.org/10.1121/1.4877585.
Silva, R. A., C. M. Buiatti, S. L. Cruz, and J. Pereira. 1996. “Pressure wave behavior and leak detection in pipelines.” Supplement, Comput. Chem. Eng. 20: S491–S496. https://doi.org/10.1016/0098-1354(96)00091-9.
Verde, C., and N. Visairo. 2001. “Bank of nonlinear observers for the detection of multiple leaks in a pipeline.” In Proc., 2001 IEEE Int. Conf. on Control Applications, 714–719. Mexico City: IEEE.
Wang, G. Z., and H. Ye. 2009. Leak detection and location for fluid transportation pipeline. [In Chinese.] Beijing: Tsinghua University Press.
Yaroslavsky, L., A. Moreno, and J. Campos. 2005. “Frequency responses and resolving power of numerical integration of sampled data.” Opt. Express 13 (8): 2892–2905. https://doi.org/10.1364/OPEX.13.002892.
Yu, L., and S. Z. Li. 2017. “Acoustic emission (AE) based small leak detection of galvanized steel pipe due to loosening of screw thread connection.” Appl. Acoust. 120: 85–89. https://doi.org/10.1016/j.apacoust.2017.01.016.
Yu, X. C., W. Liang, L. B. Zhang, H. Jin, and J. W. Qiu. 2016. “Dual-tree complex wavelet transforms and SVD based acoustic noise reduction and its application in leak detection for natural gas pipeline.” Mech. Syst. Sig. Process. 72–73: 266–285. https://doi.org/10.1016/j.ymssp.2015.
Zadkarami, M., M. Shahbazian, and K. Slahshoor. 2016. “Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network.” J. Loss Prev. Process Ind. 43: 479–487. https://doi.org/10.1016/j.jlp.2016.06.018.
Zhang, Y., S. L. Chen, J. Li, and S. J. Jin. 2014. “Leak detection monitoring system of long distance oil pipeline based on dynamic pressure transmitter.” Measurement 49: 382–389. https://doi.org/10.1016/j.measurement.2013.12.009.
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Published In
Journal of Pipeline Systems Engineering and Practice
Volume 9 • Issue 4 • November 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Nov 29, 2016
Accepted: Mar 1, 2018
Published online: Jul 3, 2018
Published in print: Nov 1, 2018
Discussion open until: Dec 3, 2018
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