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Enhanced Convolutional Neural Network for In Situ AUV Thruster Health Monitoring Using Acoustic Signals

As the demand for ocean exploration increases, studies are being actively conducted on autonomous underwater vehicles (AUVs) that can efficiently perform various missions. To successfully perform long-term, wide-ranging missions, it is necessary to apply fault diagnosis technology to AUVs. In this s...

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Autores principales: Yeo, Sang-Jae, Choi, Woen-Sug, Hong, Suk-Yoon, Song, Jee-Hun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502450/
https://www.ncbi.nlm.nih.gov/pubmed/36146422
http://dx.doi.org/10.3390/s22187073
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author Yeo, Sang-Jae
Choi, Woen-Sug
Hong, Suk-Yoon
Song, Jee-Hun
author_facet Yeo, Sang-Jae
Choi, Woen-Sug
Hong, Suk-Yoon
Song, Jee-Hun
author_sort Yeo, Sang-Jae
collection PubMed
description As the demand for ocean exploration increases, studies are being actively conducted on autonomous underwater vehicles (AUVs) that can efficiently perform various missions. To successfully perform long-term, wide-ranging missions, it is necessary to apply fault diagnosis technology to AUVs. In this study, a system that can monitor the health of in situ AUV thrusters using a convolutional neural network (CNN) was developed. As input data, an acoustic signal that comprehensively contains the mechanical and hydrodynamic information of the AUV thruster was adopted. The acoustic signal was pre-processed into two-dimensional data through continuous wavelet transform. The neural network was trained with three different pre-processing methods and the accuracy was compared. The decibel scale was more effective than the linear scale, and the normalized decibel scale was more effective than the decibel scale. Through tests on off-training conditions that deviate from the neural network learning condition, the developed system properly recognized the distribution characteristics of noise sources even when the operating speed and the thruster rotation speed changed, and correctly diagnosed the state of the thruster. These results showed that the acoustic signal-based CNN can be effectively used for monitoring the health of the AUV’s thrusters.
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spelling pubmed-95024502022-09-24 Enhanced Convolutional Neural Network for In Situ AUV Thruster Health Monitoring Using Acoustic Signals Yeo, Sang-Jae Choi, Woen-Sug Hong, Suk-Yoon Song, Jee-Hun Sensors (Basel) Article As the demand for ocean exploration increases, studies are being actively conducted on autonomous underwater vehicles (AUVs) that can efficiently perform various missions. To successfully perform long-term, wide-ranging missions, it is necessary to apply fault diagnosis technology to AUVs. In this study, a system that can monitor the health of in situ AUV thrusters using a convolutional neural network (CNN) was developed. As input data, an acoustic signal that comprehensively contains the mechanical and hydrodynamic information of the AUV thruster was adopted. The acoustic signal was pre-processed into two-dimensional data through continuous wavelet transform. The neural network was trained with three different pre-processing methods and the accuracy was compared. The decibel scale was more effective than the linear scale, and the normalized decibel scale was more effective than the decibel scale. Through tests on off-training conditions that deviate from the neural network learning condition, the developed system properly recognized the distribution characteristics of noise sources even when the operating speed and the thruster rotation speed changed, and correctly diagnosed the state of the thruster. These results showed that the acoustic signal-based CNN can be effectively used for monitoring the health of the AUV’s thrusters. MDPI 2022-09-19 /pmc/articles/PMC9502450/ /pubmed/36146422 http://dx.doi.org/10.3390/s22187073 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yeo, Sang-Jae
Choi, Woen-Sug
Hong, Suk-Yoon
Song, Jee-Hun
Enhanced Convolutional Neural Network for In Situ AUV Thruster Health Monitoring Using Acoustic Signals
title Enhanced Convolutional Neural Network for In Situ AUV Thruster Health Monitoring Using Acoustic Signals
title_full Enhanced Convolutional Neural Network for In Situ AUV Thruster Health Monitoring Using Acoustic Signals
title_fullStr Enhanced Convolutional Neural Network for In Situ AUV Thruster Health Monitoring Using Acoustic Signals
title_full_unstemmed Enhanced Convolutional Neural Network for In Situ AUV Thruster Health Monitoring Using Acoustic Signals
title_short Enhanced Convolutional Neural Network for In Situ AUV Thruster Health Monitoring Using Acoustic Signals
title_sort enhanced convolutional neural network for in situ auv thruster health monitoring using acoustic signals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502450/
https://www.ncbi.nlm.nih.gov/pubmed/36146422
http://dx.doi.org/10.3390/s22187073
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