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- W3204271037 abstract "Pipeline leak identification method using transient frequency response (TFR) has been researched in the past two decades. To extend this method to a more general water pipeline system with hydraulic uncertainties, this work (1) introduces deep learning (DL) into the TFR-based leak identification framework and (2) develops extended TFR equations in matrix form for DL learning set generation. In this framework, TFR equations are firstly solved in a pre-calibrated hydraulic model of the system to extract frequency response function (FRF) for the training set preparation. Then the simulated FRFs are fed to train fully linear DenseNet (FL-DenseNet) for feature recognition. Finally, the measured FRF of the system is fed to the trained FL-DenseNet to identify a leak to a pipe in the suspected leak area. A study on a hypothetical small system shows that the proposed framework has robustness against uncertainties of friction coefficient, wave speed, and leak flow. A significant advantage is also observed over the existing method with an inaccurate model. Then the framework is applied to a larger network. Over 90% of the synthetic leaks are identified in 5 of the 149 pipes. These results presented in the paper indicate the potential of applying this framework to a water pipeline system." @default.
- W3204271037 created "2021-10-11" @default.
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- W3204271037 date "2021-11-01" @default.
- W3204271037 modified "2023-10-18" @default.
- W3204271037 title "Deep learning identifies leak in water pipeline system using transient frequency response" @default.
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- W3204271037 doi "https://doi.org/10.1016/j.psep.2021.09.033" @default.
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