Identification of ground-based acoustic signals for water supply pipeline leakage based on sliding windows and time-frequency sequence features
The detection of water pipe leaks using machine learning to identify ground-based acoustic signals has long been a hot topic in the field of water supply safety. However, achieving reliable leak detection in complex real scenarios remains a significant challenge. Based on an acoustic signal dataset collected from an actual pipeline network, this study proposes a sliding window traversal method with intra-window normalization (SWT-IWN) for data preprocessing, which effectively enhances the separability of acoustic features between leakage and non-leakage pipe segments. On this basis, a dataset fused with spatial position information of sampling points is constructed. Furthermore, a leakage identification model named CST-Net is designed, which uses a convolutional neural network (CNN) to extract time-frequency features and a Swin-Transformer to model the positional sequence correlation of sampling points, thereby realizing collaborative representation of the two feature types. Experimental results show that CST-Net accurately identifies leakage pipe segments of 2.5 m in length, with an identification accuracy of 92.35%. The model also demonstrates strong identification performance and stability under diverse sampling conditions, well meeting the requirements of practical engineering applications.