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© 2022 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 (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Vibration measurement and analysis play an important role in diagnosing mechanical faults, but existing vibration sensors are limited by issues such as dependence on external power sources and high costs. To overcome these challenges, the use of triboelectric nanogenerator (TENG)−based vibration sensors has recently attracted attention. These vibration sensors measure a small range of vibration frequencies and are not suitable for measuring high-frequency vibrations. Herein, a self-powered vibration sensor based on an elastic steel triboelectric nanogenerator (ES−TENG) is proposed. By optimizing the elastic steel sheet structure and combining time-frequency transformation and filtering processing methods, the measurement of medium- and high-frequency vibrations is achieved. These results demonstrate that the ES−TENG can perform vibration measurements in the range of 2–10,000 Hz, with a small average error (~0.42%) between the measured frequency and external vibration frequency values. Therefore, the ES−TENG can be used as a self-powered, highly-accurate vibration sensor for intelligent machinery monitoring.

Details

Title
Ultra−Wide Range Vibration Frequency Detection Sensors Based on Elastic Steel Triboelectric Nanogenerators for Intelligent Machinery Monitoring
Author
Huang, Xili 1 ; Zhang, Cheng 2 ; Pang, Hongchen 2 ; Zhao, Zhiqiang 1   VIAFID ORCID Logo  ; Zhang, Qianxi 2 ; Li, Xiaoning 2 ; Wang, Xianzhang 2 ; Lin, Fang 1 ; Li, Bo 1 ; Pan, Xinxiang 1 

 School of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China 
 School of Mechanical and Power Engineering, Guangdong Ocean University, Zhanjiang 524088, China 
First page
2790
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20794991
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2706420366
Copyright
© 2022 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 (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.