FANPeng-hui,JIANGTao,NIUChao-qun,et al.Fault Diagnosis Method of Drainage Network Based on Liquid Level Monitoring Data and CNN-SVM[J].China Water & Wastewater,2023,39(23):30-39.
Fault Diagnosis Method of Drainage Network Based on Liquid Level Monitoring Data and CNN-SVM
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第39卷
Number:
第23期
Page:
30-39
Column:
Date of publication:
2023-12-01
- Abstract:
- In order to improve the operation and maintenance management ability of drainage pipe network, and the structural and functional defects of pipe network can be effectively monitored and identified，a fault diagnosis method of drainage network based on liquid level monitoring data and CNN-SVM was proposed by analyzing the specific requirements of drainage network monitoring tasks. Softmax classifier was replaced by SVM classifier to improve the classification performance of CNN and avoid the disadvantages of SVM in data feature extraction. In view of the complexity of drainage pipeline monitoring environment, the drainage pipeline defect test device was designed and combined with the Internet of Things monitoring system to collect data.The results showed that the model was very effective in the diagnosis and troubleshooting of drainage pipe defects, with an accuracy of 94.20%, 91.57% and 85.34% under the tasks of ten classification, thirteen classification and full classification, respectively. Compared with other diagnostic models, the CNN-SVM model had a 16.94% higher accuracy than the second?best CNN-LSTM model in all classification tasks requiring the highest classification accuracy, and also had obvious advantages in accuracy rate, recall rate and F1-Measure, which verified the generalization and effectiveness of the proposed model.
Last Update:
2023-12-01