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Towards flow measurement with passive accelerometers
RISE Research Institutes of Sweden, Säkerhet och transport, Mätteknik.ORCID-id: 0000-0002-9445-9669
RISE Research Institutes of Sweden, Säkerhet och transport, Mätteknik.
RISE Research Institutes of Sweden, Säkerhet och transport, Mätteknik.
RISE Research Institutes of Sweden, Säkerhet och transport, Mätteknik.
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2021 (engelsk)Inngår i: Flow Measurement and Instrumentation, ISSN 0955-5986, E-ISSN 1873-6998, Vol. 80, artikkel-id 101992Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The aim of this project has been to find suitable methods for flow measurement and characterization with passive accelerometers. The objectives were twofold. Firstly, the process industry could make use of such a sensor for process surveillance. Secondly, the water utilities of today lack simple and cost-efficient alternatives to equip their ageing infrastructures with online flow meters. These kinds of efforts are necessary for the realization of smart maintenance and for the decrease of the currently increasing amount of maintenance needs water utilities of today are experiencing. Liquid flowing in a pipe generates vibrations, detectable with accelerometers fitted along the pipe exterior. The correlated sound from synchronized accelerometers experience a lag which is dependent on the flow rate. Also, if the acquired sound is further processed, there exist a possibility to extract enough features to estimate some additional characteristics, in this case temperature. Experiments were performed at two nominal temperatures, 20 °C and 40 °C. A deep neural network was constructed for non-linear regression purposes to predict flow velocities based on lag and mean frequencies of the vibrations. Further, a proof of concept for this methodology was shown which reached a root mean square deviation from 100.8 L/min to 171.1 L/min for a nominal flow range of 0 to 1500 L/min. In addition, we train a k-nearest neighbour classifier to predict the nominal temperature of our validation dataset with 83 percent accuracy. The work was performed at RISE Research Institutes of Sweden, serving as Sweden's national metrology institute for liquid flow and acoustics. © 2021 The Authors

sted, utgiver, år, opplag, sider
Elsevier Ltd , 2021. Vol. 80, artikkel-id 101992
Emneord [en]
Cross-correlation, Deep learning, k-nearest neighbour, Machine learning, Vibrations, Accelerometers, Classification (of information), Deep neural networks, Flow measurement, Nearest neighbor search, Ageing infrastructures, K-nearest neighbours, National metrology institutes, Non-linear regression, Process industries, Proof of concept, Research institutes, Root mean square deviations, Flowmeters
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Identifikatorer
URN: urn:nbn:se:ri:diva-54483DOI: 10.1016/j.flowmeasinst.2021.101992Scopus ID: 2-s2.0-85107790083OAI: oai:DiVA.org:ri-54483DiVA, id: diva2:1570254
Merknad

Funding details: VINNOVA, 2019-04975; Funding details: VINNOVA; Funding text 1: The work was supported by Vinnova, the Swedish Governmental Agency for Innovation Systems (No. 2019-04975 ).

Tilgjengelig fra: 2021-06-21 Laget: 2021-06-21 Sist oppdatert: 2023-06-02bibliografisk kontrollert

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Penttinen, OlleBüker, Oliver

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