Neural network identification of water pipe blockage from smart embedded passive acoustic measurements
Research output: Contribution to journal › Article › peer-review
This study presents a new neural network approach to identify the presence and type of obstruction in pipes from measurements of passive acoustic emissions. Inserts were used in a fluid re-circulation loop to simulate different types of blockage at various flow rates within the turbulent regime, generating patterns of acoustic emissions. The data were pre-processed using Fourier analysis, and two candidate sets of statistical descriptors were extracted for each measurement. The first set used average and spread of the Fourier transform amplitudes, the second used data binning to obtain a concise representation of the spectrum of amplitudes. Experimental evidence showed the second set of descriptors was the most suitable to train the neural network to recognize with accuracy the presence and type of blockage. The obtained results compare favourably with the literature, indicating that the approach provides a tool to enhance process monitoring in water supply systems, in particular early detection of upstream blockages.
Publisher Copyright: © 2021 The Authors. The Canadian Journal of Chemical Engineering published by Wiley Periodicals LLC on behalf of Canadian Society for Chemical Engineering.
|Journal||Canadian Journal of Chemical Engineering|
|Early online date||24 May 2021|
|Publication status||E-pub ahead of print - 24 May 2021|
- acoustic emission, neural network, online monitoring, pressure drop, turbulent flow