{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T15:15:54Z","timestamp":1776438954321,"version":"3.51.2"},"reference-count":46,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology, Taiwan,","award":["MOST110-2218-E-110-011"],"award-info":[{"award-number":["MOST110-2218-E-110-011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s21217187","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:24:22Z","timestamp":1635805462000},"page":"7187","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9512-4249","authenticated-orcid":false,"given":"Chia-Ming","family":"Tsai","sequence":"first","affiliation":[{"name":"Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chiao-Sheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Jen","family":"Chung","sequence":"additional","affiliation":[{"name":"Naval Academy R.O.C., Kaohsiung 804, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yung-Da","family":"Sun","sequence":"additional","affiliation":[{"name":"Naval Meteorological and Oceanographic Office R.O.C., Kaohsiung 804, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jau-Woei","family":"Perng","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan"},{"name":"Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 807, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Friebe, A., Olsson, M., Gallic, M.L., Springett, J.L., Dahl, K., and Waller, M. 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