One-dimensional residual convolutional neural network and percussion-based method for pipeline leakage and water deposit detection

Pipeline leakage and water deposits can cause serious consequences, such as environmental pollution, safety accidents, and economic losses. Therefore, effective detection of these flaws is of critical importance. Currently, most of the detection methods rely heavily on experienced inspectors and specialized equipment, which is labor-intensive and costly. To this end, this paper presents a one-dimensional residual convolutional neural network (1D-ResNet) based percussion method, for detecting pipeline leakage and water deposit. The proposed method uses sound produced by tapping the pipe as input to 1D-ResNet, which can directly extract features from the audio signal, avoiding hand-crafted feature extraction process. The effectiveness of the proposed method is validated through experiments, showing strong performance in pipeline fault detection. Furthermore, the 1D-ResNet method exhibits better classification performance and stronger noise robustness compared to other methods. In summary, this study presents a novel approach for the detection of pipeline leakage and deposit through the innovative introduction of 1D-ResNet deep learning technology, which has significant application prospects.