Research on Active Thermal Based Distributed Fiber Optic Temperature Sensing for Leakage Detection in Water Pipeline
Publication: Earth and Space 2021
ABSTRACT
Leakage loss of urban water pipelines is a common problem, which causes tremendous waste of water resources and great economic loss. Due to the long distance and complicated pipeline network, the existing technologies cannot effectively realize the leakage detection and localization of the whole pipeline network. In this research, the feasibility of leakage detection and localization in water pipelines was demonstrated based on active thermal method and distributed fiber optic temperature-sensing technology. In the proposed method, the sensing element was a thermal cable that was fabricated by coupling the heating element with the distributed fiber optic temperature sensing element. The thermal cable was buried under the pipeline. On account of the different heat transfer characteristics of heated thermal cable in soil and water environments, the conduction in soil and convection in water, respectively, the leakage and nonleakage locations can be identified. Numerical simulations and experiments were conducted to verify the feasibility of the proposed method for leakage detection. Good agreement has been achieved.
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REFERENCES
Bristow, K. L. (1998). “Measurement of thermal properties and water content of unsaturated sandy soil using dual-probe heat-pulse probes.” Agricultural and forest meteorology 89(2): 75-84.
Cataldo, A., G. Cannazza, E. De Benedetto, and N. Giaquinto (2011). “A new method for detecting leaks in underground water pipelines.” IEEE Sensors Journal 12(6): 1660-1667.
Cataldo, A., R. Persico, G. Leucci, E. De Benedetto, G. Cannazza, L. Matera, and L. De Giorgi (2014). “Time domain reflectometry, ground penetrating radar and electrical resistivity tomography: a comparative analysis of alternative approaches for leak detection in underground pipes.” Ndt & E International 62: 14-28.
Gao, Y., Y. Liu, Y. Ma, X. Cheng, and J. Yang (2018). “Application of the differentiation process into the correlation-based leak detection in urban pipeline networks.” Mechanical Systems and Signal Processing 112: 251-264.
Ghazali, M., S. Beck, J. Shucksmith, J. Boxall, and W. Staszewski (2012). “Comparative study of instantaneous frequency based methods for leak detection in pipeline networks.” Mechanical Systems and Signal Processing 29: 187-200.
Khulief, Y., A. Khalifa, R. B. Mansour, and M. Habib (2011). “Acoustic detection of leaks in water pipelines using measurements inside pipe.” Journal of Pipeline Systems Engineering and Practice 3(2): 47-54.
Krishnaiah, S., D. Singh, and G. Jadhav (2004). “A methodology for determining thermal properties of rocks.”
Li, W., S. C. M. Ho, and G. Song (2016). “Corrosion detection of steel reinforced concrete using combined carbon fiber and fiber Bragg grating active thermal probe.” Smart Materials and Structures 25(4): 045017.
Sun, Z., P. Wang, M. C. Vuran, M. A. Al-Rodhaan, A. M. Al-Dhelaan, and I. F. Akyildiz (2011). “MISE-PIPE: Magnetic induction-based wireless sensor networks for underground pipeline monitoring.” Ad Hoc Networks 9(3): 218-227.
Wang, X., and M. S. Ghidaoui (2018). “Identification of multiple leaks in pipeline: Linearized model, maximum likelihood, and super-resolution localization.” Mechanical Systems and Signal Processing 107: 529-548.
Wang, X., J. Lin, A. Keramat, M. S. Ghidaoui, S. Meniconi, and B. Brunone (2019). “Matched-field processing for leak localization in a viscoelastic pipe: An experimental study.” Mechanical Systems and Signal Processing 124: 459-478.
Zhao, X., W. Li, G. Song, Z. Zhu, and J. Du (2013). “Scour monitoring system for subsea pipeline based on active thermometry: Numerical and experimental studies.” Sensors 13(2): 1490-1509.
Zhao, X., W. Li, L. Zhou, G.-B. Song, Q. Ba, and J. Ou (2013). “Active thermometry based DS18B20 temperature sensor network for offshore pipeline scour monitoring using K-means clustering algorithm.” International Journal of Distributed Sensor Networks 9(6): 852090.
Zhao, X., W. Li, L. Zhou, G.-B. Song, Q. Ba, S. C. M. Ho, and J. Ou (2015). “Application of support vector machine for pattern classification of active thermometry-based pipeline scour monitoring.” Structural Control and Health Monitoring 22(6): 903-918.
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© 2021 American Society of Civil Engineers.
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Published online: Apr 15, 2021
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