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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-10-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/21/7187 |
| _version_ | 1827677843591528448 |
|---|---|
| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:53:26Z |
| format | Article |
| id | doaj.art-ea40d441dbfd439aa7df334d990a27b3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:53:26Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ea40d441dbfd439aa7df334d990a27b32023-11-22T21:37:53ZengMDPI AGSensors1424-82202021-10-012121718710.3390/s21217187Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep LearningChia-Ming Tsai0Chiao-Sheng Wang1Yu-Jen Chung2Yung-Da Sun3Jau-Woei Perng4Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, TaiwanDepartment of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, TaiwanNaval Academy R.O.C., Kaohsiung 804, TaiwanNaval Meteorological and Oceanographic Office R.O.C., Kaohsiung 804, TaiwanDepartment of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, TaiwanWith 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.https://www.mdpi.com/1424-8220/21/21/7187propeller fault diagnosisunderwater thrusterdeep learning |
| spellingShingle | 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 Sensors propeller fault diagnosis underwater thruster deep learning |
| title | 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_short | 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 |
| topic | propeller fault diagnosis underwater thruster deep learning |
| url | https://www.mdpi.com/1424-8220/21/21/7187 |
| work_keys_str_mv | AT chiamingtsai multisensorfaultdiagnosisofunderwaterthrusterpropellerbasedondeeplearning AT chiaoshengwang multisensorfaultdiagnosisofunderwaterthrusterpropellerbasedondeeplearning AT yujenchung multisensorfaultdiagnosisofunderwaterthrusterpropellerbasedondeeplearning AT yungdasun multisensorfaultdiagnosisofunderwaterthrusterpropellerbasedondeeplearning AT jauwoeiperng multisensorfaultdiagnosisofunderwaterthrusterpropellerbasedondeeplearning |