Abstract
Piping systems are widely utilized in industry and home. Leakage of piping systems induced by prolonged corrosion, severe weather, or man-made damage will lead to serious consequences like explosion disasters, severe damage to industrial equipment, unforeseeable waste of resources and even threaten human life. WSNs significantly attract attentions in Industry 4.0 in recent years due to their advantages of wide distribution, remote controllability, convenient portability, easy programming, and economy. Meanwhile, as a non-intrusive measurement technique, vibration manifests a great potential for leakage detection of piping systems. In this paper, a vibration-based wireless sensing system is developed to remotely monitor the condition of piping systems in real time. According to the analytical results of vibration signals at two different positions on the piping system, the effective statistical features are extracted at the wireless sensor node to detect the leakage and its severity of the piping system. Furthermore, it can reduce the amount of data transmitted to reduce the power consumption then prolong the service life of the designed wireless sensing system. The diagnostic result can be conveniently observed on the mobile device in real time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Datta, S., Sarkar, S.: A review on different pipeline fault detection methods. J. Loss Prev. Process Ind. 41, 97–106 (2016). https://doi.org/10.1016/j.jlp.2016.03.010
BenSaleh, M.S., Manzoor Qasim, S., Abid, M., Jmal, M.W., Karray, F., Obeid, A.M.: Towards realisation of wireless sensor network-based water pipeline monitoring systems: a comprehensive review of techniques and platforms. IET Sci. Meas. Technol. 10(5), 420–426 (2016). https://doi.org/10.1049/iet-smt.2015.0255
Tejedor, J., Macias-Guarasa, J., Martins, H.F., Pastor-Graells, J., Corredera, P., Martin-Lopez, S.: Machine learning methods for pipeline surveillance systems based on distributed acoustic sensing: a review. Appl. Sci. 7(8), 841 (2017)
Rashid, S., Akram, U., Khan, S.A.: WML: wireless sensor network based machine learning for leakage detection and size estimation. Procedia Comput. Sci. 63, 171–176 (2015). https://doi.org/10.1016/j.procs.2015.08.329
Ismail, M., Dziyauddin, R.A., Salleh, N.A.A.: Performance evaluation of wireless accelerometer sensor for water pipeline leakage. In: 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), pp. 120–125 (2015). https://doi.org/10.1109/IRIS.2015.7451598.
Ustinov, M.: Tollmien-Schlichting wave generation by flow turbulence. Fluid Dyn. 49(4), 468–480 (2014)
Rudyak, V.Y., Belkin, A.: Fluid viscosity under confined conditions. Dokl. Phys. 59, 604–606 (2014)
Wachel, J., Morton, S.J., Atkins, K.E.: Piping Vibration Analysis, pp. 119–134 (1990)
Qu, Z., Feng, H., Zeng, Z., Zhuge, J., Jin, S.: A SVM-based pipeline leakage detection and pre-warning system. Measurement 43(4), 513–519 (2010). https://doi.org/10.1016/j.measurement.2009.12.022
Dinardo, G., Fabbiano, L., Vacca, G.: Fluid flow rate estimation using acceleration sensors. In: Seventh International Conference on Sensing Technology (ICST), pp. 221–225 (2013). https://doi.org/10.1109/ICSensT.2013.6727646
Shukla, H., Piratla, K.: Leakage detection in water pipelines using supervised classification of acceleration signals. Autom. Constr. 117, 103256 (2020). https://doi.org/10.1016/j.autcon.2020.103256
Karray, F., Garcia-Ortiz, A., Jmal, M.W., Obeid, A.M., Abid, M.: EARNPIPE: a testbed for smart water pipeline monitoring using wireless sensor network. Procedia Comput. Sci. 96, 285–294 (2016). https://doi.org/10.1016/j.procs.2016.08.141
Moulik, S., Majumdar, S., Pal, V., Thakran, Y.: Water leakage detection in hilly region PVC pipes using wireless sensors and machine learning. In: IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), pp. 1–2 (2020). https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258144
Mohd Ismail, M.I., Dziyauddin, R.A., Mohd Salleh, N.A., Ahmad, R., Hadri Azmi, M., Mad Kaidi, H.: Analysis and procedures for water pipeline leakage using three-axis accelerometer sensors: ADXL335 and MMA7361. IEEE Access 6, 71249–71261 (2018). https://doi.org/10.1109/ACCESS.2018.2878862
Liu, Y., Ma, X., Li, Y., Tie, Y., Zhang, Y., Gao, J.: Water pipeline leakage detection based on machine learning and wireless sensor networks’. Sensors 19(23), 23 (2019). https://doi.org/10.3390/s19235086
Salameh, H., Dhainat, M., Benkhelifa, E.: A survey on wireless sensor network-based IoT designs for gas leakage detection and fire-fighting applications. Jordanian J. Comput. Inf. Technol. 1 (2019). https://doi.org/10.5455/jjcit.71-1550235278
Tang, X., Wang, X., Cattley, R., Gu, F., Ball, A.D.: Energy harvesting technologies for achieving self-powered wireless sensor networks in machine condition monitoring: a review. Sensors 18(12), 4113 (2018). https://doi.org/10.3390/s18124113
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tang, X., Jia, Y., Feng, G., Xu, Y., Gu, F., Ball, A.D. (2023). Online Pipe Leakage Detection Using the Vibration-Based Wireless Sensing System. In: Zhang, H., Feng, G., Wang, H., Gu, F., Sinha, J.K. (eds) Proceedings of IncoME-VI and TEPEN 2021. Mechanisms and Machine Science, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-030-99075-6_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-99075-6_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-99074-9
Online ISBN: 978-3-030-99075-6
eBook Packages: EngineeringEngineering (R0)