Data-Driven Inline Leak Detection for Pipelines Using Flow-Induced Acoustics Analysis

Abstract

Fluid and water distribution networks are essential to the modern world. However, these systems are prone to leaks, which can lead to significant water loss, damage to infrastructure, and environmental pollution. The proposed solution makes use of Acoustic Emission sensors placed in discrete locations in the pipeline which measures the sound in the pipeline caused by the flow of fluids. Computation models are used to deduce the location from the input provided by the sensors. In case of leak, the leak is localized through cross correlation and TDOA methods. This solution is particularly developed for water distribution pipelines. Keyword: Acoustic data analysis, Data-driven models, Cross-Correlation, Time Difference of Arrival (TDOA)