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  5. A Self‐Powered Dynamic Displacement Monitoring System Based on Triboelectric Accelerometer

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Article
en
2017

A Self‐Powered Dynamic Displacement Monitoring System Based on Triboelectric Accelerometer

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en
2017
Vol 7 (19)
Vol. 7
DOI: 10.1002/aenm.201700565

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Yu Hua
Xu He
Wenbo Ding
+8 more

Abstract

Abstract An integrated self‐powered dynamic displacement monitoring system by utilizing a novel triboelectric accelerometer for structural health monitoring is proposed and implemented in this study, which can show the dynamic displacement and transmit the alarming signal by accurately sensing the vibration acceleration. The fabricated triboelectric accelerometer based on the noncontact freestanding triboelectric nanogenerator consists of an outer transparent sleeve tube and an inner cylindrical inertial mass that is suspended by a highly stretchable silicone fiber. One pair of copper film electrodes is deposited by physical vapor deposition on nylon film and adhered on the inner wall of the outer tube, while a fluorinated ethylene propylene film with nanowire structures is adhered on the surface of the inner cylindrical inertial mass. The experimental results show that proposed triboelectric accelerometer can accurately sense the vibration acceleration with a high sensitivity of 0.391 V s 2 m −1 . In particular, the developed accelerometer has superior performance within the low‐frequency range. One of the most striking features is that the commercial accelerometer using piezoelectric material is strongly dominated by high‐order harmonics, which can cause confusion in computer data analysis. In contrast, the triboelectric accelerometer is only dominated by the base resonance mode.

How to cite this publication

Yu Hua, Xu He, Wenbo Ding, Yongshan Hu, Dongchen Yang, Shan Lu, Changsheng Wu, Haiyang Zou, Ruiyuan Liu, Canhui Lu, Zhong Lin Wang (2017). A Self‐Powered Dynamic Displacement Monitoring System Based on Triboelectric Accelerometer. , 7(19), DOI: https://doi.org/10.1002/aenm.201700565.

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Publication Details

Type

Article

Year

2017

Authors

11

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/aenm.201700565

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