Water Pipeline Leak Detection Based on a Pseudo-Siamese Convolutional Neural Network: Integrating Handcrafted Features and Deep Representations

Abstract

The detection of leaks in water distribution systems (WDS) has always been a major concern for urban water supply companies. However, the performance of traditional leak detection classifiers highly depends on the effectiveness of handcrafted features. An alternative method is to use a convolutional neural network (CNN) to process raw signals directly to obtain deep representations that may ignore prior information about the leakage. The study proposes a novel approach to leak detection in WDS using ground acoustic signals, and demonstrates the effectiveness of combining handcrafted features and deep representations using a pseudo-siamese convolutional neural network (PCNN) model. Mel frequency cepstral coefficient (MFCCs) are selected as additional handcrafted features to traditional time- and frequency-domain (TFD) features. Based on the results of the model performance evaluation, the optimized PCNN model performs better than other methods, with an accuracy of 99.70%. A quantitative analysis of the PCNN demonstrates the effectiveness of handcrafted features and deep representations. Model visualization and interpretation analysis show that feature fusion occurs in the feedforward of the PCNN, hence improving the model’s performance. The present work can effectively support the development of novel intelligent leak detection equipment for WDS.