ZHAOLin-shuo,YEGuo-xuan,SHENYong-gang,et al.Leakage Detection of Water Supply Pipeline Based on Time-Frequency Convolutional Neural Network[J].China Water & Wastewater,2023,39(17):53-58.
Leakage Detection of Water Supply Pipeline Based on Time-Frequency Convolutional Neural Network
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第39卷
Number:
第17期
Page:
53-58
Column:
Date of publication:
2023-09-01
- Keywords:
- water supply pipeline; leakage detection; time-frequency analysis; convolutional neural network
- Abstract:
- This paper designed an automatic identification model of leakage signal based on ground vibration signal and time-frequency convolutional neural network to solve the problems of misjudgment, time consuming and low efficiency of water supply pipeline leakage detection. The time-frequency image containing leakage characteristics was obtained by continuous wavelet transform of the collected ground vibration signals, and was input into the convolutional neural network to optimize the network hyperparameters, and the leakage identification model was eventually obtained. The average accuracy of the final model in the test set was 97.3%, and the average recognition rates of the leak point difficult to distinguish by detectors and the suspicious signal near the leak point were 91.0% and 92.3%, respectively, indicating that the model had a good ability of leak diagnosis. Compared with support vector machine, decision tree and other methods, the proposed method had higher accuracy.
Last Update:
2023-09-01