Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning

With the rapid development of unmanned surfaces and underwater vehicles, fault diagnoses for underwater thrusters are important to prevent sudden damage, which can cause huge losses. The propeller causes the most common type of thruster damage. Thus, it is important to monitor the propeller’s health...

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Autores principales:	Chia-Ming Tsai, Chiao-Sheng Wang, Yu-Jen Chung, Yung-Da Sun, Jau-Woei Perng
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	propeller fault diagnosis underwater thruster deep learning Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/ea40d441dbfd439aa7df334d990a27b3
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spelling	oai:doaj.org-article:ea40d441dbfd439aa7df334d990a27b32021-11-11T19:10:40ZMulti-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning10.3390/s212171871424-8220https://doaj.org/article/ea40d441dbfd439aa7df334d990a27b32021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7187https://doaj.org/toc/1424-8220With the rapid development of unmanned surfaces and underwater vehicles, fault diagnoses for underwater thrusters are important to prevent sudden damage, which can cause huge losses. The propeller causes the most common type of thruster damage. Thus, it is important to monitor the propeller’s health reliably. This study proposes a fault diagnosis method for underwater thruster propellers. A deep convolutional neural network was proposed to monitor propeller conditions. A Hall element and hydrophone were used to obtain the current signal from the thruster and the sound signal in water, respectively. These raw data were fast Fourier transformed from the time domain to the frequency domain and used as the input to the neural network. The output of the neural network indicated the propeller’s health conditions. This study demonstrated the results of a single signal and the fusion of multiple signals in a neural network. The results showed that the multi-signal input had a higher accuracy than the one-signal input. With multi-signal inputs, training two types of signals with a separated neural network and then merging them at the end yielded the best results (99.88%), as compared to training two types of signals with a single neural network.Chia-Ming TsaiChiao-Sheng WangYu-Jen ChungYung-Da SunJau-Woei PerngMDPI AGarticlepropeller fault diagnosisunderwater thrusterdeep learningChemical technologyTP1-1185ENSensors, Vol 21, Iss 7187, p 7187 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	propeller fault diagnosis underwater thruster deep learning Chemical technology TP1-1185
spellingShingle	propeller fault diagnosis underwater thruster deep learning Chemical technology TP1-1185 Chia-Ming Tsai Chiao-Sheng Wang Yu-Jen Chung Yung-Da Sun Jau-Woei Perng Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning
description	With the rapid development of unmanned surfaces and underwater vehicles, fault diagnoses for underwater thrusters are important to prevent sudden damage, which can cause huge losses. The propeller causes the most common type of thruster damage. Thus, it is important to monitor the propeller’s health reliably. This study proposes a fault diagnosis method for underwater thruster propellers. A deep convolutional neural network was proposed to monitor propeller conditions. A Hall element and hydrophone were used to obtain the current signal from the thruster and the sound signal in water, respectively. These raw data were fast Fourier transformed from the time domain to the frequency domain and used as the input to the neural network. The output of the neural network indicated the propeller’s health conditions. This study demonstrated the results of a single signal and the fusion of multiple signals in a neural network. The results showed that the multi-signal input had a higher accuracy than the one-signal input. With multi-signal inputs, training two types of signals with a separated neural network and then merging them at the end yielded the best results (99.88%), as compared to training two types of signals with a single neural network.
format	article
author	Chia-Ming Tsai Chiao-Sheng Wang Yu-Jen Chung Yung-Da Sun Jau-Woei Perng
author_facet	Chia-Ming Tsai Chiao-Sheng Wang Yu-Jen Chung Yung-Da Sun Jau-Woei Perng
author_sort	Chia-Ming Tsai
title	Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning
title_short	Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning
title_full	Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning
title_fullStr	Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning
title_full_unstemmed	Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning
title_sort	multi-sensor fault diagnosis of underwater thruster propeller based on deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ea40d441dbfd439aa7df334d990a27b3
work_keys_str_mv	AT chiamingtsai multisensorfaultdiagnosisofunderwaterthrusterpropellerbasedondeeplearning AT chiaoshengwang multisensorfaultdiagnosisofunderwaterthrusterpropellerbasedondeeplearning AT yujenchung multisensorfaultdiagnosisofunderwaterthrusterpropellerbasedondeeplearning AT yungdasun multisensorfaultdiagnosisofunderwaterthrusterpropellerbasedondeeplearning AT jauwoeiperng multisensorfaultdiagnosisofunderwaterthrusterpropellerbasedondeeplearning
_version_	1718431619005546496

Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning

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