Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767266
Title: Deriving further information from the leak signal in water distribution pipes
Author: Butterfield, Joseph
ISNI:       0000 0004 7658 5615
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Access from Institution:
Abstract:
Leaking pipes from water distribution systems are a huge issue which has maintained worldwide attention. A common method of detecting leaks is through the cross correlation of leak vibroacoustic emission signals, created as the water discharges through the hole/crack and recorded by acclerometers or hydrophones placed either side of a suspected leak location. It is thought that a number of factors influence leak signals, however there has been little comprehensive study in controlled conditions evaluating how and what way these factors influence the characteristics of leaks signals. Moreover, during the process of leak noise correlation, accelerometers and hydrophones are recording information about the leak contained in the leak signal that is currently not understood. Knowledge of these factors would be highly beneficial to water companies in order to allow for prioritisation of leak repair. The research presented herein aimed to derive a method in order to predict the flow rate, area and shape of a leak using the vibo-acoustic emission signal recorded when performing cross correlation. A unique methodology was developed which allowed for the isolation of individual physical variables and how this can influence a leak signal. Specifically, the study focused on developing a fundamental understanding of how leak flow rate, area, shape, pipe material and backfill type influenced the characterstics of leak signals. The results showed that the leak flow rate, shape, backfill types and pipe material all influence the leak signal. The influence of leak area on the leak signal appeared negligible when leak flow rates were standardised. Signal processing and machine learning algorithms were applied to the leak signals and the results showed that it was possible to predict leak flow rate regardless of leak area, leak shape and backfill type. Moreover, alternate algorithms showed that it was possible to predict leak shape and leak area from the vibro-acoustic emission signal. This research has therefore presented a useful and valid tool to predict leak flow rate, leak area and leak shape which allows water companies to prioritise leak repair and maintenance activities, providing an opportunity to reduce the volume of water lost through leaks by repairing the larger flow rate leaks first. Whilst this method shows effective results, it does not provide an exhaustive comparison of the number of algorithms and techniques which may also make similar predictions.
Supervisor: Beck, Stephen ; Collins, Richard Sponsor: Not available
Qualification Name: Thesis (Eng.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.767266  DOI: Not available
Share: