Ultra-high resolution strain sensor network assisted with an LS-SVM based hysteresis model

Optical fiber sensor network has attracted considerable research interests for geoscience applications. However, the sensor capacity and ultra-low frequency noise limits the sensing performance for geoscience data acquisition. To achieve a high-resolution and lager sensing capacity, a strain sensor...

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Autores principales:	Tao Liu, Hao Li, Tao He, Cunzheng Fan, Zhijun Yan, Deming Liu, Qizhen Sun
Formato:	article
Lenguaje:	EN
Publicado:	Institue of Optics and Electronics, Chinese Academy of Sciences 2021
Materias:	optical fiber sensing sensor networking geoscience research quasi-static sensing Optics. Light QC350-467
Acceso en línea:	https://doaj.org/article/79b7725b60114a198f7b7a338c881960
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spelling	oai:doaj.org-article:79b7725b60114a198f7b7a338c8819602021-11-17T07:52:44ZUltra-high resolution strain sensor network assisted with an LS-SVM based hysteresis model2096-457910.29026/oea.2021.200037https://doaj.org/article/79b7725b60114a198f7b7a338c8819602021-05-01T00:00:00Zhttp://www.oejournal.org/article/doi/10.29026/oea.2021.200037https://doaj.org/toc/2096-4579Optical fiber sensor network has attracted considerable research interests for geoscience applications. However, the sensor capacity and ultra-low frequency noise limits the sensing performance for geoscience data acquisition. To achieve a high-resolution and lager sensing capacity, a strain sensor network is proposed based on phase-sensitive optical time domain reflectometer (φ-OTDR) technology and special packaged fiber with scatter enhanced points (SEPs) array. Specifically, an extra identical fiber with SEPs array which is free of strain is used as the reference fiber, for compensating the ultra-low frequency noise in the φ-OTDR system induced by laser source frequency shift and environment temperature change. Moreover, a hysteresis operator based least square support vector machine (LS-SVM) model is introduced to reduce the compensation residual error generated from the thermal hysteresis nonlinearity between the sensing fiber and reference fiber. In the experiment, the strain sensor network possesses a sensing capacity with 55 sensor elements. The phase bias drift with frequency below 0.1 Hz is effectively compensated by LS-SVM based hysteresis model, and the signal to noise ratio (SNR) of a strain vibration at 0.01 Hz greatly increases by 24 dB compared to that of the sensing fiber for direct compensation. The proposed strain sensor network proves a high dynamic resolution of 10.5 pε·Hz-1/2 above 10 Hz, and ultra-low frequency sensing resolution of 166 pε at 0.001 Hz. It is the first reported a large sensing capacity strain sensor network with sub-nε sensing resolution in mHz frequency range, to the best of our knowledge.Tao LiuHao LiTao HeCunzheng FanZhijun YanDeming LiuQizhen SunInstitue of Optics and Electronics, Chinese Academy of Sciencesarticleoptical fiber sensingsensor networkinggeoscience researchquasi-static sensingOptics. LightQC350-467ENOpto-Electronic Advances, Vol 4, Iss 5, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	optical fiber sensing sensor networking geoscience research quasi-static sensing Optics. Light QC350-467
spellingShingle	optical fiber sensing sensor networking geoscience research quasi-static sensing Optics. Light QC350-467 Tao Liu Hao Li Tao He Cunzheng Fan Zhijun Yan Deming Liu Qizhen Sun Ultra-high resolution strain sensor network assisted with an LS-SVM based hysteresis model
description	Optical fiber sensor network has attracted considerable research interests for geoscience applications. However, the sensor capacity and ultra-low frequency noise limits the sensing performance for geoscience data acquisition. To achieve a high-resolution and lager sensing capacity, a strain sensor network is proposed based on phase-sensitive optical time domain reflectometer (φ-OTDR) technology and special packaged fiber with scatter enhanced points (SEPs) array. Specifically, an extra identical fiber with SEPs array which is free of strain is used as the reference fiber, for compensating the ultra-low frequency noise in the φ-OTDR system induced by laser source frequency shift and environment temperature change. Moreover, a hysteresis operator based least square support vector machine (LS-SVM) model is introduced to reduce the compensation residual error generated from the thermal hysteresis nonlinearity between the sensing fiber and reference fiber. In the experiment, the strain sensor network possesses a sensing capacity with 55 sensor elements. The phase bias drift with frequency below 0.1 Hz is effectively compensated by LS-SVM based hysteresis model, and the signal to noise ratio (SNR) of a strain vibration at 0.01 Hz greatly increases by 24 dB compared to that of the sensing fiber for direct compensation. The proposed strain sensor network proves a high dynamic resolution of 10.5 pε·Hz-1/2 above 10 Hz, and ultra-low frequency sensing resolution of 166 pε at 0.001 Hz. It is the first reported a large sensing capacity strain sensor network with sub-nε sensing resolution in mHz frequency range, to the best of our knowledge.
format	article
author	Tao Liu Hao Li Tao He Cunzheng Fan Zhijun Yan Deming Liu Qizhen Sun
author_facet	Tao Liu Hao Li Tao He Cunzheng Fan Zhijun Yan Deming Liu Qizhen Sun
author_sort	Tao Liu
title	Ultra-high resolution strain sensor network assisted with an LS-SVM based hysteresis model
title_short	Ultra-high resolution strain sensor network assisted with an LS-SVM based hysteresis model
title_full	Ultra-high resolution strain sensor network assisted with an LS-SVM based hysteresis model
title_fullStr	Ultra-high resolution strain sensor network assisted with an LS-SVM based hysteresis model
title_full_unstemmed	Ultra-high resolution strain sensor network assisted with an LS-SVM based hysteresis model
title_sort	ultra-high resolution strain sensor network assisted with an ls-svm based hysteresis model
publisher	Institue of Optics and Electronics, Chinese Academy of Sciences
publishDate	2021
url	https://doaj.org/article/79b7725b60114a198f7b7a338c881960
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Ultra-high resolution strain sensor network assisted with an LS-SVM based hysteresis model

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