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Leakage from water distribution systems is a worldwide issue with consequences including loss of revenue, health and environmental concerns. Leaks have typically been found through leak noise correlation by placing sensors either side of the leak and recording and analysing its vibro-acoustic emission. While this method is widely used to identify the location of the leak, the sensors also record data that could be related to the leak's flow rate, yet no reliable method exists to predict leak flow rate in water distribution pipes using vibro-acoustic emission. The aim of this research is to predict leak flow rate in medium-density polyethylene pipe using vibro-acoustic emission signals. A novel experimental methodology is presented whereby circular holes of four sizes are tested at several leak flow rates. Following the derivation of a number of features, least squares support vector machines are used in order to predict leak flow rate. The results show a strong correlation highlighting the potential of this technique as a rapid and practical tool for water companies to assess and prioritise leak repair.

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The location of leaks in water distribution pipes are traditionally identified by cross correlating two vibro-acoustic signals measured with accelerometers either side of the leak, but this method provides little information about the features of the signal (such as the leak flow rate) and does not differentiate between the leak signal and background noise. Due to its capability to provide time and frequency information, wavelet analysis can be used to extract features about the leak. However one of the greatest challenges is the selection of the mother wavelet, and there has been limited studies investigating how varying the mother wavelet can produce alternate results in the context of leaks in water distribution systems. This study demonstrates the potential of the wavelet transform to distinguish between the different features composed in the signal, including the leak noise, ambient noise, pump noise and leak type on plastic pipe when applied to data from a test rig. Variables such as the influence of pressure or distance from the leak are also included in order to demonstrate the efficacy of the wavelet transform in providing information about the signal. Moreover, this research gains an understanding of how the choice of different mother wavelet can influence the information provided by the leak signal. The results demonstrate that an appropriate mother wavelet can be identified and that the wavelet transform can localise features of the signal, including pump noise, background noise and the leaks characteristics.

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