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A novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks

The heat exchanger (HE) is an important component of almost every energy generation system. Periodic inspection of the HEs is particularly important to keep high efficiency of the entire system. In this paper, a novel ultrasonic water immersion inspection method is presented based on circumferential...

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Autores principales: ugli Malikov, Azamatjon Kakhramon, Cho, Younho, Kim, Young H., Kim, Jeongnam, Kim, Hyung-Kyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450277/
https://www.ncbi.nlm.nih.gov/pubmed/36727198
http://dx.doi.org/10.1177/00368504221146081
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author ugli Malikov, Azamatjon Kakhramon
Cho, Younho
Kim, Young H.
Kim, Jeongnam
Kim, Hyung-Kyu
author_facet ugli Malikov, Azamatjon Kakhramon
Cho, Younho
Kim, Young H.
Kim, Jeongnam
Kim, Hyung-Kyu
author_sort ugli Malikov, Azamatjon Kakhramon
collection PubMed
description The heat exchanger (HE) is an important component of almost every energy generation system. Periodic inspection of the HEs is particularly important to keep high efficiency of the entire system. In this paper, a novel ultrasonic water immersion inspection method is presented based on circumferential wave (CW) propagation to detect defective HE. Thin patch-type piezoelectric elements with multiple resonance frequencies were adopted for the ultrasonic inspection of narrow-spaced HE in an immersion test. Water-filled HE was used to simulate defective HE because water is the most reliable indicator of the defect. The HE will leak water no matter what the defect pattern is. Furthermore, continuous wavelet transform (CWT) was used to investigate the received CW, and inverse CWT was applied to separate frequency bands corresponding to the thickness and lateral resonance modes of the piezoelectric element. Different arrangements of intact and leaky HE were tested with several pairs of thin piezoelectric patch probes in various instrumental setups. Also, direct waveforms in the water without HE were used as reference signals, to indicate instrumental gain and probe sensitivity. Moreover, all filtered CW corresponding to resonance modes together with the direct waveforms in the water were used to train the deep neural networks (DNNs). As a result, an automatic HE state classification method was obtained, and the accuracy of the applied DNN was estimated as 99.99%.
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spelling pubmed-104502772023-08-26 A novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks ugli Malikov, Azamatjon Kakhramon Cho, Younho Kim, Young H. Kim, Jeongnam Kim, Hyung-Kyu Sci Prog Ultrasonic Guided Waves in Complex Media with Applications in Materials characterization Using NDE Methods The heat exchanger (HE) is an important component of almost every energy generation system. Periodic inspection of the HEs is particularly important to keep high efficiency of the entire system. In this paper, a novel ultrasonic water immersion inspection method is presented based on circumferential wave (CW) propagation to detect defective HE. Thin patch-type piezoelectric elements with multiple resonance frequencies were adopted for the ultrasonic inspection of narrow-spaced HE in an immersion test. Water-filled HE was used to simulate defective HE because water is the most reliable indicator of the defect. The HE will leak water no matter what the defect pattern is. Furthermore, continuous wavelet transform (CWT) was used to investigate the received CW, and inverse CWT was applied to separate frequency bands corresponding to the thickness and lateral resonance modes of the piezoelectric element. Different arrangements of intact and leaky HE were tested with several pairs of thin piezoelectric patch probes in various instrumental setups. Also, direct waveforms in the water without HE were used as reference signals, to indicate instrumental gain and probe sensitivity. Moreover, all filtered CW corresponding to resonance modes together with the direct waveforms in the water were used to train the deep neural networks (DNNs). As a result, an automatic HE state classification method was obtained, and the accuracy of the applied DNN was estimated as 99.99%. SAGE Publications 2023-02-01 /pmc/articles/PMC10450277/ /pubmed/36727198 http://dx.doi.org/10.1177/00368504221146081 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Ultrasonic Guided Waves in Complex Media with Applications in Materials characterization Using NDE Methods
ugli Malikov, Azamatjon Kakhramon
Cho, Younho
Kim, Young H.
Kim, Jeongnam
Kim, Hyung-Kyu
A novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks
title A novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks
title_full A novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks
title_fullStr A novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks
title_full_unstemmed A novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks
title_short A novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks
title_sort novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks
topic Ultrasonic Guided Waves in Complex Media with Applications in Materials characterization Using NDE Methods
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450277/
https://www.ncbi.nlm.nih.gov/pubmed/36727198
http://dx.doi.org/10.1177/00368504221146081
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