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A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine
The development and application of marine current energy are attracting more and more attention around the world. Due to the hardness of its working environment, it is important and difficult to study the fault diagnosis of a marine current generation system. In this paper, an underwater image is ch...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412786/ https://www.ncbi.nlm.nih.gov/pubmed/30781577 http://dx.doi.org/10.3390/s19040826 |
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author | Zheng, Yilai Wang, Tianzhen Xin, Bin Xie, Tao Wang, Yide |
author_facet | Zheng, Yilai Wang, Tianzhen Xin, Bin Xie, Tao Wang, Yide |
author_sort | Zheng, Yilai |
collection | PubMed |
description | The development and application of marine current energy are attracting more and more attention around the world. Due to the hardness of its working environment, it is important and difficult to study the fault diagnosis of a marine current generation system. In this paper, an underwater image is chosen as the fault-diagnosing signal, after different sensors are compared. This paper proposes a diagnosis method based on the sparse autoencoder (SA) and softmax regression (SR). The SA is used to extract the features and SR is used to classify them. Images are used to monitor whether the blade is attached by benthos and to determine its corresponding degree of attachment. Compared with other methods, the experiment results show that the proposed method can diagnose the blade attachment with higher accuracy. |
format | Online Article Text |
id | pubmed-6412786 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64127862019-04-03 A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine Zheng, Yilai Wang, Tianzhen Xin, Bin Xie, Tao Wang, Yide Sensors (Basel) Article The development and application of marine current energy are attracting more and more attention around the world. Due to the hardness of its working environment, it is important and difficult to study the fault diagnosis of a marine current generation system. In this paper, an underwater image is chosen as the fault-diagnosing signal, after different sensors are compared. This paper proposes a diagnosis method based on the sparse autoencoder (SA) and softmax regression (SR). The SA is used to extract the features and SR is used to classify them. Images are used to monitor whether the blade is attached by benthos and to determine its corresponding degree of attachment. Compared with other methods, the experiment results show that the proposed method can diagnose the blade attachment with higher accuracy. MDPI 2019-02-17 /pmc/articles/PMC6412786/ /pubmed/30781577 http://dx.doi.org/10.3390/s19040826 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zheng, Yilai Wang, Tianzhen Xin, Bin Xie, Tao Wang, Yide A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine |
title | A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine |
title_full | A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine |
title_fullStr | A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine |
title_full_unstemmed | A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine |
title_short | A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine |
title_sort | sparse autoencoder and softmax regression based diagnosis method for the attachment on the blades of marine current turbine |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412786/ https://www.ncbi.nlm.nih.gov/pubmed/30781577 http://dx.doi.org/10.3390/s19040826 |
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