Interpretable deep learning for acoustic leak detection in water distribution systems

Abstract

Leak detection is crucial for ensuring the safety of water systems and conserving water resources. However, current research on machine learning methods for leak detection focuses excessively on model development while neglecting model interpretability, which leads to transparency and credibility issues in practical applications. This study proposes the multi-channel convolution neural network (MCNN) model and compares the performance of the MCNN model with the existing benchmark algorithm (i.e., frequency convolutional neural network (FCNN)) using both experimental and real field data. Additionally, Multi-channel Gradient-weighted Class Activation Mapping (MGrad-CAM) was introduced to visualize the decision-making criterion of the model and identify critical signatures of acoustic signals. The study also employed clustering methods to analyze the impact mechanisms of various factors (i.e., pressure, leak flow rate, and distance) on acoustic signals from a machine learning perspective. Results show that the MCNN method outperformed the FCNN across laboratory and real-world datasets, achieving a high accuracy rate of 95.4% in real-field scenarios. Using the MGrad-CAM, the interpretability of the DL model was analyzed, successfully identifying and visualizing the critical signatures of leak acoustic signals with more precise and fine-grained details. Additionally, this study clusters leak signals into two patterns and confirms that the bandwidth of the leak acoustic signal increases with higher pressure, closer proximity to the leak, and higher leak flow rates. It has also been discovered that the high-frequency components of the signal assist the model in more accurately detecting leaks. This study provides a new perspective for understanding the decision-making criterion of the leak detection model and the mechanism of the leak acoustic signal generation.