Abstract
Pipes are widely used in industries for conveying fluids and gas from one place to another. The pipeline network is subjected to several problems, such as surface load, lousy quality, pitting corrosion, and water hammer, which cause cracks in a pipeline or joint. Complexities of nonlinear parameters inherent in a pipeline prohibit the detection and isolation of leaks in real-time. Early detection of leaks is important to avoid product loss and other severe damage. To address this issue, this paper proposes a proportional-integral (PI) fuzzy observer to detect and estimate a leak. In addition, a Takagi-Sugeno (T-S) fuzzy technique is applied to an ARX-Laguerre observer to improve the fault estimation in the presence of uncertainties, where we assume that measurements are only available at the inlet and outlet of a pipeline. Experimental results demonstrate that the proposed method detects and estimates leaks early and accurately.
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Acknowledgements
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 (No. 20172510102130). It was also funded by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A3B03931927).
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Piltan, F., Kim, JM. (2020). Pipeline Leak Detection and Estimation Using Fuzzy-Based PI Observer. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A., Sari, I. (eds) Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. Advances in Intelligent Systems and Computing, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-030-23756-1_132
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DOI: https://doi.org/10.1007/978-3-030-23756-1_132
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