Abstract
ABSTRACTSmall leaks in buried water distribution pipelines run continuously for long periods of time without being detected. They do not produce any appreciable flow or pressure changes at the monitored locations. The non-stationarity of the monitoring data, background noise, and the uncertainties in interpreting sensory information adds complexity to detecting leaks. This paper explores the application of singular spectrum analysis (SSA) in extracting leak components from noisy measurements. SSA is a non-parametric and adaptive method, able to decompose a signal into interpretable components without making linearity or stationarity assumptions. When applied to noisy hydro-acoustic signals, it is shown that the leak signatures are extracted efficiently. A semi-supervised approach for leak detection is presented, in which the SSA decomposition of leak-free historical data is combined with ensemble one-class support vector machine. The results demonstrate the effectiveness of SSA for leak detection in water distribution pipelines.
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