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| Leakage Detection and Localization of Fluid Pipeline Based on Elman Neural Network |
| Received:December 20, 2019 Revised:January 15, 2020 |
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| DOI：10.7643/issn.1672-9242.2020.04.002 |
| KeyWord:hydraulic transportation, leak localization, negative pressure wave, neural network |
| Author | Institution |
| CAO Zheng |
Xi′an Jiaotong University, Xi′an , China;Shaanxi Key Laboratory of Energy Chemical Process Intensification, Xi′an , China |
| DENG Jian-qiang |
Xi′an Jiaotong University, Xi′an , China;Shaanxi Key Laboratory of Energy Chemical Process Intensification, Xi′an , China |
| WANG Ze-liang |
Xi′an Jiaotong University, Xi′an , China;Shaanxi Key Laboratory of Energy Chemical Process Intensification, Xi′an , China |
| XUAN Bing-wei |
Xi′an Jiaotong University, Xi′an , China;Shaanxi Key Laboratory of Energy Chemical Process Intensification, Xi′an , China |
| GUO Xi-jian |
Xi′an Jiaotong University, Xi′an , China;Shaanxi Key Laboratory of Energy Chemical Process Intensification, Xi′an , China |
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| Abstract: |
| The paper aims to detect and locate the leakage points through the Elman neural network prediction test. Based on the negative pressure wave method and the feedback Elman neural network method of fluid pressure wave, the leakage localization of water pipelines were researched. By using one dimensional hydraulic model in Flowmaster, a pipeline system with a total length of 1100m was established, and numerical simulation for various leakage conditions was carried out. The leakage location was estimated after data noise reduction and singular point capture through wavelet transform. Meanwhile, with the help of the feedback Elman neural network, network training and prediction were carried out under different leakage conditions. Five groups of leakage locations were predicted by the trained neural network. The maximum error for the leak location prediction was 1.83%. The accuracy and effectiveness of the feedback neural network method for the pipeline leakage localization was verified through the comparison between the actual values and the results calculated based on Elman neural network. |
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