Deep learning identifies leak in water pipeline system using transient frequency response

Ziyuan Liao, Hexiang Yan, Zhenheng Tang, Xiaowen Chu, Tao Tao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)355-365
Number of pages11
JournalProcess Safety and Environmental Protection
Volume155
DOIs
Publication statusPublished - Nov 2021

Scopus Subject Areas

  • Environmental Engineering
  • Environmental Chemistry
  • Chemical Engineering(all)
  • Safety, Risk, Reliability and Quality

User-Defined Keywords

  • Deep learning
  • Frequency response
  • Leak identification
  • Water pipelines

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