Experimental study on water pipeline leak using in-pipe acoustic signal analysis and artificial neural network prediction

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dc.contributor.authorWang, Wenming
dc.contributor.authorSun, Haibo
dc.contributor.authorGuo, Jianqiang
dc.contributor.authorLao, Liyun
dc.contributor.authorWu, Shide
dc.contributor.authorZhang, Jifeng
dc.date.accessioned2021-09-23T16:03:58Z
dc.date.available2021-09-23T16:03:58Z
dc.date.issued2021-08-30
dc.description.abstractWater pipeline leakage is a common and significant global problem. In-pipe inspection based on hydrophone is one of the most direct, accurate, and reliable solutions for leak detection and recognition. In this study, a scheme of in-pipe detector was designed to pick up and identify acoustic signal due to leak. To investigate the characteristic of acoustic signal, an experimental platform was built to simulate the leaks and obtain acoustic signals under different leak conditions in an industrial scale water pipeline. Because a decreased pressure as leak has an unstable fluctuation in time domain, the frequency composition of the signal was analyzed in frequency domain, and then the change of frequency amplitude can be referenced to recognize the leaks. Moreover, the effects of leak size, pipeline pressure, and water flow rate on the characteristic of acoustic signal were investigated. The results show that the signal’s intensity under leak conditions are significantly higher than that of no leak case, and it will increase as the increased leak size; the signal intensity under no leak case will increase with the growth of pipeline pressure; the flow velocity has little effect on the signal intensity. To increase the recognition accuracy, an artificial neural network model was developed for the leak prediction, and 18 cases through additional tests were selected to validate the accuracy of model. Comparing experimental and prediction results, maximum relative error is within 10.0%. It indicates that the prediction model has a reasonable accuracy for the leak recognition.en_UK
dc.identifier.citationWang W, Sun H, Guo J, et al., (2021) Experimental study on water pipeline leak using in-pipe acoustic signal analysis and artificial neural network prediction. Measurement, Volume 186, December 2021, Article number 110094.en_UK
dc.identifier.issn0263-2241
dc.identifier.urihttps://doi.org/10.1016/j.measurement.2021.110094
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/17101
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjecttime and frequency domainen_UK
dc.subjectin-pipe leak predictionen_UK
dc.subjectwater pipelineen_UK
dc.subjectartificial neural network modelen_UK
dc.titleExperimental study on water pipeline leak using in-pipe acoustic signal analysis and artificial neural network predictionen_UK
dc.typeArticleen_UK

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