High-Accuracy Event Classification of Distributed Optical Fiber Vibration Sensing Based on Time–Space Analysis

Distributed optical fiber vibration sensing (DVS) can measure vibration information along with an optical fiber. Accurate classification of vibration events is a key issue in practical applications of DVS. In this paper, we propose a convolutional neural network (CNN) to analyze DVS data and achieve...
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Autor*in:	Zhao Ge [verfasserIn] Hao Wu [verfasserIn] Can Zhao [verfasserIn] Ming Tang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	distributed vibration sensing event classification convolutional neural network

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 22(2022), 5, p 2053
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:5, p 2053

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s22052053

Katalog-ID:	DOAJ085252743

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520			\|a Distributed optical fiber vibration sensing (DVS) can measure vibration information along with an optical fiber. Accurate classification of vibration events is a key issue in practical applications of DVS. In this paper, we propose a convolutional neural network (CNN) to analyze DVS data and achieve high-accuracy event recognition fully. We conducted experiments outdoors and collected more than 10,000 sets of vibration data. Through training, the CNN acquired the features of the raw DVS data and achieved the accurate classification of multiple vibration events. The recognition accuracy reached 99.9% based on the time–space data, a higher than used time-domain, frequency–domain, and time–frequency domain data. Moreover, considering that the performance of the DVS and the testing environment would change over time, we experimented again after one week to verify the method’s generalization performance. The classification accuracy using the previously trained CNN is 99.2%, which is of great value in practical applications.
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spelling	10.3390/s22052053 doi (DE-627)DOAJ085252743 (DE-599)DOAJba9543f5dbca497abdce86b43cc6a18d DE-627 ger DE-627 rakwb eng TP1-1185 Zhao Ge verfasserin aut High-Accuracy Event Classification of Distributed Optical Fiber Vibration Sensing Based on Time–Space Analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Distributed optical fiber vibration sensing (DVS) can measure vibration information along with an optical fiber. Accurate classification of vibration events is a key issue in practical applications of DVS. In this paper, we propose a convolutional neural network (CNN) to analyze DVS data and achieve high-accuracy event recognition fully. We conducted experiments outdoors and collected more than 10,000 sets of vibration data. Through training, the CNN acquired the features of the raw DVS data and achieved the accurate classification of multiple vibration events. The recognition accuracy reached 99.9% based on the time–space data, a higher than used time-domain, frequency–domain, and time–frequency domain data. Moreover, considering that the performance of the DVS and the testing environment would change over time, we experimented again after one week to verify the method’s generalization performance. The classification accuracy using the previously trained CNN is 99.2%, which is of great value in practical applications. distributed vibration sensing event classification convolutional neural network Chemical technology Hao Wu verfasserin aut Can Zhao verfasserin aut Ming Tang verfasserin aut In Sensors MDPI AG, 2003 22(2022), 5, p 2053 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:5, p 2053 https://doi.org/10.3390/s22052053 kostenfrei https://doaj.org/article/ba9543f5dbca497abdce86b43cc6a18d kostenfrei https://www.mdpi.com/1424-8220/22/5/2053 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 5, p 2053
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allfieldsGer	10.3390/s22052053 doi (DE-627)DOAJ085252743 (DE-599)DOAJba9543f5dbca497abdce86b43cc6a18d DE-627 ger DE-627 rakwb eng TP1-1185 Zhao Ge verfasserin aut High-Accuracy Event Classification of Distributed Optical Fiber Vibration Sensing Based on Time–Space Analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Distributed optical fiber vibration sensing (DVS) can measure vibration information along with an optical fiber. Accurate classification of vibration events is a key issue in practical applications of DVS. In this paper, we propose a convolutional neural network (CNN) to analyze DVS data and achieve high-accuracy event recognition fully. We conducted experiments outdoors and collected more than 10,000 sets of vibration data. Through training, the CNN acquired the features of the raw DVS data and achieved the accurate classification of multiple vibration events. The recognition accuracy reached 99.9% based on the time–space data, a higher than used time-domain, frequency–domain, and time–frequency domain data. Moreover, considering that the performance of the DVS and the testing environment would change over time, we experimented again after one week to verify the method’s generalization performance. The classification accuracy using the previously trained CNN is 99.2%, which is of great value in practical applications. distributed vibration sensing event classification convolutional neural network Chemical technology Hao Wu verfasserin aut Can Zhao verfasserin aut Ming Tang verfasserin aut In Sensors MDPI AG, 2003 22(2022), 5, p 2053 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:5, p 2053 https://doi.org/10.3390/s22052053 kostenfrei https://doaj.org/article/ba9543f5dbca497abdce86b43cc6a18d kostenfrei https://www.mdpi.com/1424-8220/22/5/2053 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 5, p 2053
allfieldsSound	10.3390/s22052053 doi (DE-627)DOAJ085252743 (DE-599)DOAJba9543f5dbca497abdce86b43cc6a18d DE-627 ger DE-627 rakwb eng TP1-1185 Zhao Ge verfasserin aut High-Accuracy Event Classification of Distributed Optical Fiber Vibration Sensing Based on Time–Space Analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Distributed optical fiber vibration sensing (DVS) can measure vibration information along with an optical fiber. Accurate classification of vibration events is a key issue in practical applications of DVS. In this paper, we propose a convolutional neural network (CNN) to analyze DVS data and achieve high-accuracy event recognition fully. We conducted experiments outdoors and collected more than 10,000 sets of vibration data. Through training, the CNN acquired the features of the raw DVS data and achieved the accurate classification of multiple vibration events. The recognition accuracy reached 99.9% based on the time–space data, a higher than used time-domain, frequency–domain, and time–frequency domain data. Moreover, considering that the performance of the DVS and the testing environment would change over time, we experimented again after one week to verify the method’s generalization performance. The classification accuracy using the previously trained CNN is 99.2%, which is of great value in practical applications. distributed vibration sensing event classification convolutional neural network Chemical technology Hao Wu verfasserin aut Can Zhao verfasserin aut Ming Tang verfasserin aut In Sensors MDPI AG, 2003 22(2022), 5, p 2053 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:5, p 2053 https://doi.org/10.3390/s22052053 kostenfrei https://doaj.org/article/ba9543f5dbca497abdce86b43cc6a18d kostenfrei https://www.mdpi.com/1424-8220/22/5/2053 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 5, p 2053
language	English
source	In Sensors 22(2022), 5, p 2053 volume:22 year:2022 number:5, p 2053
sourceStr	In Sensors 22(2022), 5, p 2053 volume:22 year:2022 number:5, p 2053
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topic_facet	distributed vibration sensing event classification convolutional neural network Chemical technology
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authorswithroles_txt_mv	Zhao Ge @@aut@@ Hao Wu @@aut@@ Can Zhao @@aut@@ Ming Tang @@aut@@
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callnumber-first	T - Technology
author	Zhao Ge
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topic_title	TP1-1185 High-Accuracy Event Classification of Distributed Optical Fiber Vibration Sensing Based on Time–Space Analysis distributed vibration sensing event classification convolutional neural network
topic	misc TP1-1185 misc distributed vibration sensing misc event classification misc convolutional neural network misc Chemical technology
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title	High-Accuracy Event Classification of Distributed Optical Fiber Vibration Sensing Based on Time–Space Analysis
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title_sort	high-accuracy event classification of distributed optical fiber vibration sensing based on time–space analysis
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title_auth	High-Accuracy Event Classification of Distributed Optical Fiber Vibration Sensing Based on Time–Space Analysis
abstract	Distributed optical fiber vibration sensing (DVS) can measure vibration information along with an optical fiber. Accurate classification of vibration events is a key issue in practical applications of DVS. In this paper, we propose a convolutional neural network (CNN) to analyze DVS data and achieve high-accuracy event recognition fully. We conducted experiments outdoors and collected more than 10,000 sets of vibration data. Through training, the CNN acquired the features of the raw DVS data and achieved the accurate classification of multiple vibration events. The recognition accuracy reached 99.9% based on the time–space data, a higher than used time-domain, frequency–domain, and time–frequency domain data. Moreover, considering that the performance of the DVS and the testing environment would change over time, we experimented again after one week to verify the method’s generalization performance. The classification accuracy using the previously trained CNN is 99.2%, which is of great value in practical applications.
abstractGer	Distributed optical fiber vibration sensing (DVS) can measure vibration information along with an optical fiber. Accurate classification of vibration events is a key issue in practical applications of DVS. In this paper, we propose a convolutional neural network (CNN) to analyze DVS data and achieve high-accuracy event recognition fully. We conducted experiments outdoors and collected more than 10,000 sets of vibration data. Through training, the CNN acquired the features of the raw DVS data and achieved the accurate classification of multiple vibration events. The recognition accuracy reached 99.9% based on the time–space data, a higher than used time-domain, frequency–domain, and time–frequency domain data. Moreover, considering that the performance of the DVS and the testing environment would change over time, we experimented again after one week to verify the method’s generalization performance. The classification accuracy using the previously trained CNN is 99.2%, which is of great value in practical applications.
abstract_unstemmed	Distributed optical fiber vibration sensing (DVS) can measure vibration information along with an optical fiber. Accurate classification of vibration events is a key issue in practical applications of DVS. In this paper, we propose a convolutional neural network (CNN) to analyze DVS data and achieve high-accuracy event recognition fully. We conducted experiments outdoors and collected more than 10,000 sets of vibration data. Through training, the CNN acquired the features of the raw DVS data and achieved the accurate classification of multiple vibration events. The recognition accuracy reached 99.9% based on the time–space data, a higher than used time-domain, frequency–domain, and time–frequency domain data. Moreover, considering that the performance of the DVS and the testing environment would change over time, we experimented again after one week to verify the method’s generalization performance. The classification accuracy using the previously trained CNN is 99.2%, which is of great value in practical applications.
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title_short	High-Accuracy Event Classification of Distributed Optical Fiber Vibration Sensing Based on Time–Space Analysis
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High-Accuracy Event Classification of Distributed Optical Fiber Vibration Sensing Based on Time–Space Analysis

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