Skip to main content

A Mobile Robot Based Monitoring Platform for Pipeline Leakage Diagnosis Based on Cross-correlation Analysis

  • Conference paper
  • First Online:
Proceedings of IncoME-V & CEPE Net-2020 (IncoME-V 2020)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 105))

Included in the following conference series:

  • 1441 Accesses

Abstract

Nowadays, various pipelines are broadly used in the transportation of resources in the production process and life applications, such as natural gas, compressed air, water, oil and so on. Pipe leakages usually occur due to the improper manufacturing process, installation of valves or other devices, prolonged use, etc. Pipeline leakage will cause energy loss, pollution and other severe problems, which will lead to equipment damage and even casualties. Therefore, the detection of pipeline leakage is significantly important. Traditional pipeline leak detection requires visual inspection or many fixed detection sensors. This may cause missed inspection by human errors and also will greatly increase the cost on the expensive devices. Compared with visual inspection and specific testing devices, mobile robots have the advantages of portability, economic cost and wide application for effective and efficient pipeline leakage detection. Robots can be deployed on lines or near equipment prone to pipeline leaks which can be detected online according to the path set. This paper presents an effective pipeline leak detection method to monitor the leakage of a two-stage reciprocating compressor with a mobile robot. An android mobile phone is installed on the mobile robot platform. After the robot moves to the monitoring point, the microphone of the mobile phone starts to collect environmental sound. The cross-correlation analysis is then performed on the LABVIEW software platform to implement online detection of different leakage faults. The experimental results show that the extreme value of the correlation number is effective and efficient for real-time detection of the presence of leaks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Quigley, M., Conley, K., Gerkey, B.P., et al.: ROS: an open-source Robot Operating System. In: ICRA Workshop on Open Source Software (2009)

    Google Scholar 

  2. Pereira, E.L.L., Deschamps, C.J.: Numerical analysis and correlations for radial and tangential leakage of gas in scroll compressors. Int. J. Refrig. 110, 239–247 (2020)

    Article  Google Scholar 

  3. Ebrahimi-Moghadam, A., Farzaneh-Gord, M., Deymi-Dashtebayaz, M.: Correlations for estimating natural gas leakage from above-ground and buried urban distribution pipelines. J. Nat. Gas Sci. Eng. 34, 185–196 (2016)

    Article  Google Scholar 

  4. Ma, D., Deng, J., Zhang, Z.: Correlation analysis for online CO2 leakage monitoring in geological sequestration. Energy Procedia 37, 4374–4382 (2013)

    Article  Google Scholar 

  5. Zhang, B., An, L., Shen, G., et al.: Error analysis of acoustic pyrometer based on cross- correlation method. Electr. Power Sci. Eng. 1, 45–47 (2006)

    Google Scholar 

  6. Jinshan, L., Chunhong, D., Ni, Z.: Fault diagnosis of gearbox based on multifractal detrended Cross-correlation analysis. J. Mech. Transm. 40(1), 91–94 (2016)

    Google Scholar 

  7. Bourne, M.M., et al.: Cross-correlation measurements with the EJ-299-33 plastic scintillator. Nucl. Instrum. Methods Phys. Res. Sect. A Accelerators Spectrometers Detectors Assoc. Equip. 784, 460–464 (2015)

    Article  Google Scholar 

  8. Clarke, S.D., Flaska, M., Pozzi, S.A., Peerani, P.: Neutron and gamma-ray cross-correlation measurements of plutonium oxide powder. Nucl. Instrum. Methods Phys. Res. Sect. A Accelerators Spectrometers Detectors Assoc. Equip. 604(3), 618–623 (2009)

    Article  Google Scholar 

  9. Corke, P.: Integrating ROS and MATLAB [ROS Topics]. IEEE Robot. Autom. Mag. 22(2), 18–20 (2015)

    Article  Google Scholar 

  10. Cavanini, L., Cimini, G., Freddi, A., et al.: rapros: a ROS package for rapid prototyping. In: Koubaa, A. (ed.) Robot Operating System (ROS). Springer, Cham (2016)

    Google Scholar 

  11. MathWorks: Robotics System Toolbox User’s Guide. User’s Guide, Version 1.2 (2016)

    Google Scholar 

  12. Tang, W.J., Liu, Z.T.: A convenient method for tracking color-based object in living video based on ROS and MATLAB/Simulink. In: 2017 2nd International Conference on Advanced Robotics and Mechatronics (ICARM). IEEE (2018)

    Google Scholar 

Download references

Acknowledgments

This work was supported by the School Scientific Research Fund Project of Beijing Institute of Technology, Zhuhai, under Grant No. XK-2018-29 and XK-2018-34, and Guangdong Key Scientific Research Project Fund Project under Grant No. ZX-2019-011.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guocai Zhang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tang, W., Zhang, G., Gu, F., Tang, X. (2021). A Mobile Robot Based Monitoring Platform for Pipeline Leakage Diagnosis Based on Cross-correlation Analysis. In: Zhen, D., et al. Proceedings of IncoME-V & CEPE Net-2020. IncoME-V 2020. Mechanisms and Machine Science, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-030-75793-9_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-75793-9_44

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75792-2

  • Online ISBN: 978-3-030-75793-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics