Pipeline Fault Diagnosis Using Wavelet Entropy and Ensemble Deep Neural Technique

  • Bach Phi Duong
  • Jong-Myon KimEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)


The maintenance of pipelines is essential for the safe and cost effective transport of important fluids such as water, oil, and gas. The early detection of pipeline faults is vital for avoiding material and economic losses, and more importantly for ensuring the safety of both human life and the environment. This paper proposes a methodology for early fault detection in pipelines using an acoustic emission (AE) based technique. The proposed method incorporates wavelet entropy analysis of the AE signals and ensemble deep neural networks for the effective detection of different types of faults in a pipeline. The proposed method is tested on an experimental testbed, and the results indicate that it can detect various faults in the pipeline with an average accuracy of 96%.


Pipeline fault diagnosis Acoustic emission Wavelet entropy Ensemble deep neural network 



This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (Nos. 20162220100050, 20161120100350, 20172510102130). It was also funded in part by The Leading Human Resource Training Program of Regional Neo industry through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (NRF-2016H1D5A1910564), and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A3B03931927).

The authors declare that there is no conflict of interest regarding the publication of this manuscript.


  1. 1.
    Xu, Q., Zhang, L., Liang, W.: Acoustic detection technology for gas pipeline leakage. Process Saf. Environ. Prot. 91, 253–261 (2013)CrossRefGoogle Scholar
  2. 2.
    Murvay, P.-S., Silea, I.: A survey on gas leak detection and localization techniques. J. Loss Prev. Process Ind. 25, 966–973 (2012)CrossRefGoogle Scholar
  3. 3.
    Taghvaei, M., Beck, S.B.M., Staszewski, W.J.: Leak detection in pipelines using cepstrum analysis. Meas. Sci. Technol. 17, 367 (2006)CrossRefGoogle Scholar
  4. 4.
    Lee, P.J., Vítkovský, J.P., Lambert, M.F., Simpson, A.R., Liggett, J.A.: Leak location using the pattern of the frequency response diagram in pipelines: a numerical study. J. Sound Vib. 284, 1051–1073 (2005)CrossRefGoogle Scholar
  5. 5.
    Valizadeh, S., Moshiri, B., Salahshoor, K.: Leak detection in transportation pipelines using feature extraction and KNN classification. In: Pipelines (2009)Google Scholar
  6. 6.
    Ai, C., Zhao, H., Ma, R., Dong, X.: Pipeline damage and leak detection based on sound spectrum LPCC and HMM. In: Sixth International Conference on Intelligent Systems Design and Applications, pp. 829–833 (2006)Google Scholar
  7. 7.
    Sato, T., Mita, A.: Leak detection using the pattern of sound signals in water supply systems. In: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2007, p. 65292K. International Society for Optics and Photonics (2007)Google Scholar
  8. 8.
    Oterkus, E., Yang, Z.: Corrosion detection in pipelines based on measurement of natural frequencies. Ann. Limnol. Oceanogr. 2, 1–6 (2017)CrossRefGoogle Scholar
  9. 9.
    Yi-min, H., Yong-shou, L., Bao-hui, L., Yan-jiang, L., Zhu-feng, Y.: Natural frequency analysis of fluid conveying pipeline with different boundary conditions. Nucl. Eng. Des. 240, 461–467 (2010)CrossRefGoogle Scholar
  10. 10.
    Rosso, O.A., Blanco, S., Yordanova, J., Kolev, V., Figliola, A., Schürmann, M., Başar, E.: Wavelet entropy: a new tool for analysis of short duration brain electrical signals. J. Neurosci. Methods 105, 65–75 (2001)CrossRefGoogle Scholar
  11. 11.
    Whitley, D.: A genetic algorithm tutorial. Stat. Comput. 4, 65–85 (1994)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical EngineeringUniversity of UlsanUlsanSouth Korea

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