水利学报2024，Vol.55Issue(8)：999-1008,10.DOI:10.13243/j.cnki.slxb.20230676
基于VMD及深度学习的供水管道小尺度泄漏检测研究
Small-scale pipeline leak detection based on VMD and deep learning
摘要
Abstract
To address the challenge of detecting leakage signals under normal pressure and small-scale leaks,this paper focuses on the detection of water supply pipeline leaks.The experimental data of leakage under the conditions of 100-220 kPa pressure and 40-80 m3/h volume flow were obtained,and the variations in pressure signals under small-scale leak conditions were analyzed.The experimental data is denoised by using Variational Mode Decompo-sition(VMD)to reduce noise interference and enhance leak signal characteristics,followed by standardization process.The study combines typical recurrent neural networks,including Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Gated Recurrent Unit(GRU),with Convolutional Neural Network(CNN)to construct three deep learning leakage detection models CNN-LSTM,CNN-BiLSTM,and CNN-GRU.These models were evaluated for their predictive performance,among them,the CNN-GRU model exhibited the highest predictive accuracy of 99.56％for all experimental data.The results indicate that the models demonstrate high accuracy in detecting leaks under normal pressure and small-scale leak conditions.CNN proves to be instrumental in extracting pertinent features efficiently and accurately,thereby improving the prediction accuracy of the leakage detection model.The research provides valuable support for the intelligent management of pipeline leakage detection system.关键词
泄漏检测/小尺度泄漏/变分模态分解/深度学习/供水管道Key words
leak detection/small-scale leakage/variational mode decomposition/deep learning/water supply pipe-line分类
建筑与水利引用本文复制引用
郑书闽,颜建国,郭鹏程,徐燕,李江,刘振兴..基于VMD及深度学习的供水管道小尺度泄漏检测研究[J].水利学报,2024,55(8):999-1008,10.基金项目
国家自然科学基金项目(51839010) （51839010）
陕西省创新能力支撑计划项目(2024RS-CXTD-31) （2024RS-CXTD-31）
陕西高校青年创新团队项目(2020-29) （2020-29）
新疆水专项(2020.C-001) （2020.C-001）
