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Optical Fiber Vibration Signal Recognition Based on the Fusion of Multi–Scale Features

Because of the problem of low recognition accuracy in the recognition of intrusion vibration events by the distributed Sagnac type optical fiber sensing system, this paper combines the traditional optical fiber vibration signal recognition idea and the characteristics of automatic feature extraction...

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Detalles Bibliográficos
Autores principales: Ma, Xinrong, Mo, Jiaqing, Zhang, Jiangwei, Huang, Jincheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9415791/
https://www.ncbi.nlm.nih.gov/pubmed/36015773
http://dx.doi.org/10.3390/s22166012
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author Ma, Xinrong
Mo, Jiaqing
Zhang, Jiangwei
Huang, Jincheng
author_facet Ma, Xinrong
Mo, Jiaqing
Zhang, Jiangwei
Huang, Jincheng
author_sort Ma, Xinrong
collection PubMed
description Because of the problem of low recognition accuracy in the recognition of intrusion vibration events by the distributed Sagnac type optical fiber sensing system, this paper combines the traditional optical fiber vibration signal recognition idea and the characteristics of automatic feature extraction by a convolutional neural network (CNN) to construct a new endpoint detection algorithm and a method of fusing multiple–scale features CNN to recognize fiber vibration signals. Firstly, a new endpoint detection algorithm combining spectral centroid and energy spectral entropy product is used to detect the vibration part of the original signal, which is used to improve the detection effect of endpoint detection. Then, CNNs of different scales are used to extract the multi–level and multi–scale features of the signal. Aiming at the problem of information loss in the pooling process, a new method of combining differential pooling features is used. Finally, a multi–layer perceptron (MLP) is used to recognize the extracted features. Experiments show that the method has an average recognition accuracy rate of 98.75% for the four types of vibration signals. Compared with traditional EMD and VMD pattern recognition and 1D–CNN methods, the accuracy of the optical fiber vibration signal recognition is higher.
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spelling pubmed-94157912022-08-27 Optical Fiber Vibration Signal Recognition Based on the Fusion of Multi–Scale Features Ma, Xinrong Mo, Jiaqing Zhang, Jiangwei Huang, Jincheng Sensors (Basel) Article Because of the problem of low recognition accuracy in the recognition of intrusion vibration events by the distributed Sagnac type optical fiber sensing system, this paper combines the traditional optical fiber vibration signal recognition idea and the characteristics of automatic feature extraction by a convolutional neural network (CNN) to construct a new endpoint detection algorithm and a method of fusing multiple–scale features CNN to recognize fiber vibration signals. Firstly, a new endpoint detection algorithm combining spectral centroid and energy spectral entropy product is used to detect the vibration part of the original signal, which is used to improve the detection effect of endpoint detection. Then, CNNs of different scales are used to extract the multi–level and multi–scale features of the signal. Aiming at the problem of information loss in the pooling process, a new method of combining differential pooling features is used. Finally, a multi–layer perceptron (MLP) is used to recognize the extracted features. Experiments show that the method has an average recognition accuracy rate of 98.75% for the four types of vibration signals. Compared with traditional EMD and VMD pattern recognition and 1D–CNN methods, the accuracy of the optical fiber vibration signal recognition is higher. MDPI 2022-08-12 /pmc/articles/PMC9415791/ /pubmed/36015773 http://dx.doi.org/10.3390/s22166012 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
Ma, Xinrong
Mo, Jiaqing
Zhang, Jiangwei
Huang, Jincheng
Optical Fiber Vibration Signal Recognition Based on the Fusion of Multi–Scale Features
title Optical Fiber Vibration Signal Recognition Based on the Fusion of Multi–Scale Features
title_full Optical Fiber Vibration Signal Recognition Based on the Fusion of Multi–Scale Features
title_fullStr Optical Fiber Vibration Signal Recognition Based on the Fusion of Multi–Scale Features
title_full_unstemmed Optical Fiber Vibration Signal Recognition Based on the Fusion of Multi–Scale Features
title_short Optical Fiber Vibration Signal Recognition Based on the Fusion of Multi–Scale Features
title_sort optical fiber vibration signal recognition based on the fusion of multi–scale features
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9415791/
https://www.ncbi.nlm.nih.gov/pubmed/36015773
http://dx.doi.org/10.3390/s22166012
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