In this section
@article{Tong:2023:10.1016/j.seta.2022.102919,
author = {Tong, Z and Liu, H and Cao, XE and Westerdahld, D and Jin, X},
doi = {10.1016/j.seta.2022.102919},
journal = {Sustainable Energy Technologies and Assessments},
title = {Cavitation diagnosis for water distribution pumps: An early-stage approach combing vibration signal-based neural network with high-speed photography},
url = {http://dx.doi.org/10.1016/j.seta.2022.102919},
volume = {55},
year = {2023}
}
TY - JOUR
AB - As an essential component of municipal water distribution systems, centrifugal water pumps are of great significance to achieve urban sustainability. Cavitation is a common phenomenon in water pumps that cause energy inefficiency and mechanical failures. To prevent cavitation damages, an early-stage cavitation diagnosis approach combing vibration signal-based neural network with high-speed photography was proposed. An adaptive neural network was developed using vibration measurement and cavitation states predefined using high-speed cavitation images collected at an in-house laboratory pump system with transparent casings. The correlation among synchronized cavitation images, vibration signals and pump performance was investigated. Our analysis shows that the head-drop detection method commonly used in the industry greatly underestimated the damage of cavitation with the fact that a 3% head drop corresponded to a cavitation intensity of 42.1%. Both the number of predefined cavitation states for training and the structure of neural networks greatly affected diagnosis accuracy and computing load. A two-stage ANN structure with eight cavitation states displayed the best performance with a much faster training speed compared with common shallow learning methods and consistent diagnosis accuracy of over 95% in real time. A water-energy-carbon nexus model was built to demonstrate provincial energy-saving potentials associated with cavitation prevention in China.
AU - Tong,Z
AU - Liu,H
AU - Cao,XE
AU - Westerdahld,D
AU - Jin,X
DO - 10.1016/j.seta.2022.102919
PY - 2023///
SN - 2213-1388
TI - Cavitation diagnosis for water distribution pumps: An early-stage approach combing vibration signal-based neural network with high-speed photography
T2 - Sustainable Energy Technologies and Assessments
UR - http://dx.doi.org/10.1016/j.seta.2022.102919
VL - 55
ER -
Climate Science
Earth and Life Sciences
Energy and Low-Carbon Futures
Resources and Pollution
Economics and Finance
Health
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