Active acoustic leak detection and localization in a plastic pipe using time delay estimation. (February 2022)

• Record Type: Journal Article
• Authors: Kafle, Marshal Deep; Fong, Stanley; Narasimhan, Sriram
• Is Part Of: Applied acoustics. Volume 187(2022)
• Publication Date: 2022-02
• Subjects: Active acoustics -- Plane waves -- Leak detection and localization -- Prony's method -- Time delay extraction -- Mean shift clustering; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2021.108482
• Languages: English
• ISSNs: 0003-682X

Abstract

Abstract: Water distribution systems are critical to the functioning of our society, conveying drinking water to the tap from remote sources. Due to their age and other environmental and operational stresses, pipe components in these systems are prone to deterioration, resulting in leaks and bursts. Timely detection and localization of such leaks and bursts is crucial to managing the loss of this valuable resource, maintaining hydraulic capacity, and to mitigate serious health risks which can potentially arise from such events. Passive acoustic methods have received disproportionate attention both in terms of research as well as practical implementation for leak (or, bursts) detection and localization. However, since they rely on the acoustic energy generated by the leak source, detection and localization could be affected due to signal attenuation over distance and low signal-to-noise ratio, especially in plastic pipes. On the other hand, active methods offer a significant advantage in terms of the signal-to-noise ratio at the cost of a nominal increase in the complexity. This paper presents a novel active method for leak detection and localization in water distribution systems, which relies on the propagation of low-frequency sound waves in water pipes to detect and localize leaks. The primary difficulty in detecting and estimating the location of a leak is overcome through a statistical treatment of time delays associated with multiple acoustic paths in a reverberant environment and estimated using two approaches: (i) classical signal decomposition technique (Prony's method) and (ii) a clustering pre-processing approach called spectral mean-shift clustering. The former works on the cross-correlation of acoustic data recorded at two locations, while the latter operates on acoustic sensor data from a single location. Both methods are tested and validated using experimental data obtained from a laboratory test bed and are shown to be effective in accurately detecting and localizing leaks in a plastic pipe.