Particle-Based Triboelectric Vibration Sensor for Speech Recognition and Remote Machine Condition Monitoring
type: CreativeWork
description: Self-powered vibration sensors, particularly those utilising triboelectric nanogenerators, hold great promise for real-time monitoring of dynamic loads and structural health without external power sources. Based on the triboelectric effect and electrostatic induction, a triboelectric vibration sensor can convert mechanical energy into electrical energy. This technology holds significant promise for diverse applications such as voice recognition, real-time dynamic load monitoring, and structural damage or degradation detection. However, significant challenges exist in designing TENG vibration sensors that can operate over a wide frequency range and overcome electromagnetic interference and signal attenuation issues during long-distance transmission. To address these challenges, I introduce a high-bandwidth, self-powered triboelectric particle-surface vibration sensor. The sensor comprises a flat round capsule filled with dielectric particles, with an inner tribo-layer backed by electrodes. When attached to a vibrating structure, particle impacts on the inner surfaces cause opposite charges to form, generating an alternating voltage through the dynamic motion of the charged particles. The sensor exhibits a flat frequency response ranging from 500 Hz to 7000 Hz, significantly exceeding the bandwidths of recently reported triboelectric vibration sensors. Through further structural optimisation, introducing a single-particle cell has enabled the sensor to achieve similar vibration sensitivity at different angles to gravity while maintaining the flat-frequency response of the original design across a wide frequency range. Applications in voice recognition and monitoring mechanical operating conditions have demonstrated the potential of the new vibration sensor. A novel optical method is introduced to address the challenge of long-distance signal attenuation in measuring the outputs of TENG sensors using electrical cables. This approach integrates a triboelectric vibration…
abstract: Self-powered vibration sensors, particularly those utilising triboelectric nanogenerators, hold great promise for real-time monitoring of dynamic loads and structural health without external power sources. Based on the triboelectric effect and electrostatic induction, a triboelectric vibration sensor can convert mechanical energy into electrical energy. This technology holds significant promise for diverse applications such as voice recognition, real-time dynamic load monitoring, and structural damage or degradation detection. However, significant challenges exist in designing TENG vibration sensors that can operate over a wide frequency range and overcome electromagnetic interference and signal attenuation issues during long-distance transmission. To address these challenges, I introduce a high-bandwidth, self-powered triboelectric particle-surface vibration sensor. The sensor comprises a flat round capsule filled with dielectric particles, with an inner tribo-layer backed by electrodes. When attached to a vibrating structure, particle impacts on the inner surfaces cause opposite charges to form, generating an alternating voltage through the dynamic motion of the charged particles. The sensor exhibits a flat frequency response ranging from 500 Hz to 7000 Hz, significantly exceeding the bandwidths of recently reported triboelectric vibration sensors. Through further structural optimisation, introducing a single-particle cell has enabled the sensor to achieve similar vibration sensitivity at different angles to gravity while maintaining the flat-frequency response of the original design across a wide frequency range. Applications in voice recognition and monitoring mechanical operating conditions have demonstrated the potential of the new vibration sensor. A novel optical method is introduced to address the challenge of long-distance signal attenuation in measuring the outputs of TENG sensors using electrical cables. This approach integrates a triboelectric vibration…
author: Gao, Shanshi
language: en
keywords: Particle-Based Triboelectric Vibration Sensor, Speech Recognition, Remote Machine Condition Monitoring
license: http://purl.org/coar/access_right/c_f1cf
license: https://creativecommons.org/licenses/by/4.0/
date published: 2025
date modified: 2026-07-24T05:33:55Z
provider: University of New South Wales
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: UNSW, Sydney

PropertyValue
property ID: Handle
value: http://hdl.handle.net/1959.4/104044

PropertyValue
property ID: DOI
value: 10.26190/unsworks/30841