Review of model-based and data-driven approaches for leak detection and location in water distribution systems

Leak detection and location in water distribution systems (WDSs) is of utmost importance for reducing water loss, which is, however, a major challenge for water utility companies. To this end, researchers have proposed a multitude of methods to detect such leaks in WDSs. Model-based and data-driven approaches, in particular, have found widespread uses in this area. In this paper, we reviewed both these approaches and classified the techniques used by them according to their leak detection methods. It is seen that model-based approaches require highly calibrated hydraulic models, and their accuracies are sensitive to modeling and measurement uncertainties. On the contrary, data-driven approaches do not require an in-depth understanding of the WDS. However, they tend to result in high false positive rates. Furthermore, neither of these approaches can handle anomalous variations caused by unexpected water demands.

Pipeline Leak Detection and Location Based on Model-Free Isolation of Abnormal Acoustic Signals

Pipeline leaks will lead to energy waste, environmental pollution and a threat to human safety. This paper proposes a pipeline leak detection and location method based on the model-free isolation of abnormal (leak and operation) signals. An acoustic signal is first decomposed into “sub-signals” according to its zero-crossing points. Then, based on the definition of signal-to-noise ratio (SNR), the function between the SNR of sub-signal and the number of abnormal sub-signals is established, following which the position of each abnormal sub-signal in the acoustic signal is obtained by tracing its index. Based on this and the cross-correlation analysis, the operation sub-signals can be filtered, which is helpful for the precise leak location. The experimental results demonstrate the computational efficiency and lower false/missing alarm rate of the proposed method that provides an innovative solution for pipeline leak detection.