Acoustic emission technique for leak detection in an end shield of a pressurised heavy water reactor

This paper discusses a successful application of the Acoustic Emission Technique (AET) for the detection and location of leak paths present on an inaccessible side of an end shield of a Pressurised Heavy Water Reactor (PHWR). The methodology was based on the fact that air- and water-leak AE signals have different characteristic features. Baseline data was generated from a sound end shield of a PHWR for characterising the background noise. A mock-up end shield system with saw-cut leak paths was used to verify the validity of the methodology. It was found that air-leak signals under pressurisation (as low as 3 psi) could be detected by frequency domain analysis. Signals due to air leaks from various locations of defective end shield were acquired and analysed. It was possible to detect and locate leak paths. The presence of detected leak paths was further confirmed by an alternative test.

The dynamic behaviour of a buried water pipe and its effect on leak location using acoustic methods

Leakage in buried pipes is one of the main concerns for water companies due to the scarcity of potable water sources. Older metallic pipelines have been replaced by plastic pipes in such systems, which makes it more difficult to locate leaks using acoustics and vibration. This is mainly because of the high attenuation of leak signals caused by the damping in the pipe wall. To investigate acoustic methods in leak location in controlled conditions, a bespoke test rig was constructed in the UK. In this paper, data from this test-rig is used to discuss some issues that arise when using two contemporary correlators. Of particular interest, is the way in which a resonance in the system can have a profound effect on the estimate of the position of the leak depending on the way in which the leak noise signals are processed. © (2013) Trans Tech Publications.

Desenvolvimento de um simulador virtual de vazamento para testar correlacionadores acústicos comerciais

Pós-graduação em Engenharia Mecânica - FEIS

Feature extraction in pressure signals for leak detection in water networks

Techniques based on signal analysis for leak detection in water supply systems typically use long pressure and/or flow data series of variable length. This paper presents the feature extraction from pressure signals and their application to the identification of changes related to the onset of a leak. Example signals were acquired from an experimental laboratory circuit, and features were extracted from temporal domain and from transformed signals. Statistical analysis of features values and a classification method were applied. It was verified the feasibility of using feature vectors for distinguish data acquired in the absence or presence of a leak.

An investigation into the effects of resonances on the time delay estimate for leak detection in buried plastic water distribution pipes

In water distribution systems, old metallic pipes have been replaced by plastic pipes due to their deterioration over time. Although acoustic methods are effective in finding leaks in metallic pipes, they have been found to be problematic when applied to plastic pipes due to the high damping within the pipe wall and the surrounding medium. This is responsible for the leak signal not traveling long distances. Moreover, the leak energy in plastic pipes is generally located at a narrow frequency range located at low frequencies. However, the presence of resonances can narrow even more this frequency range. In order to minimise the influence of background noise and resonances on the calculation of the time delay estimate, band-pass filters are often used to supress undesirable frequency components of the noise. The objective of this paper is to investigate the influence of resonances in the pipe system (pipe, valves, connections and hydrants), on the time delay estimate calculated using acoustic signals. Analytical models and actual leak data collected in a bespoke rig located in the United Kingdom are used to investigate this feature.