A Joint Time-Frequency Attention for Leakage Detection in Water Distribution Networks Using Time Series Decomposition

Abstract

Detecting leakages in a water distribution network (WDN) is a challenging task due to the complexity of data patterns caused by the pipeline leakages and the volatility of the daily demands. Usually, the data under normal operations are collected and different machine learning algorithms are developed to predict anomalies due to the leaks. However, these methods are overwhelmingly rely on the time domain modeling and ignore the information in the frequency domain, and lack a comprehensive modeling of the data patterns such as shapelet, trend, seasonality and point outliers. In this paper, we propose a joint time-frequency attention (JTFA) approach to detect the WDN leakages. In essence, the received signals are decomposed into trend and residual components to represent the incipient and abrupt leaks separately. Attention models are then applied on both time and frequency domain signals to learn the corresponding patterns. In particular, the spectrum is divided into different frequency bands to better attend the detailed information in the higher frequency bands. The desired signals are subsequently reconstructed and compared to the input signal to generate an anomaly score. Experiments from simulated water supply networks are organized and the results demonstrate that the proposed approach performs better than existing leak detection methods and time-frequency analysis methods.