Small-scale pipeline leak detection based on VMD and deep learning
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.
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