Abstract
The performance of the Acoustic Emission (AE) technique is significantly dependent on the sensors attached to the structural surface. Although conventional commercially AE sensors, like R15a and WSa sensors, have been extensively employed in monitoring many different structures, they are unavailable in restricted-assess areas. In contrast, thin PZT sensors are small, inexpensive and lightweight. These thin PZT sensors have a great potential for passive sensing to detect AE signals. However, their utility in AE monitoring is limited due to their low signal-to-noise ratio and information incompleteness because of their simple construction. This work discusses the issues and possible solutions with regards to the specific selection and application of thin PZT sensors for passive sensing. The compatibility of different thin PZT sensors and conventional bulky sensors is investigated using pencil break lead (PBL) tests. The comparison between the recorded signals is carried out in both the time domain and frequency domain for these sensors. To improve the reliability and performance of the thin PZT sensors, a methodology employing multiple thin PZT sensors of different sizes is proposed based on machine learning techniques and sensor data fusion.
Original language | English |
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Title of host publication | European Workshop on Structural Health Monitoring, EWSHM 2022, Volume 1 |
Editors | Piervincenzo Rizzo, Alberto Milazzo |
Publisher | Springer |
Pages | 619-629 |
Number of pages | 11 |
ISBN (Electronic) | 978-3-031-07254-3 |
ISBN (Print) | 978-3-031-07253-6 |
DOIs | |
Publication status | Published - 2023 |
Event | 10th European Workshop on Structural Health Monitoring, EWSHM 2022 - Palermo, Italy Duration: 4 Jul 2022 → 7 Jul 2022 |
Publication series
Name | Lecture Notes in Civil Engineering |
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Volume | 253 LNCE |
ISSN (Print) | 2366-2557 |
ISSN (Electronic) | 2366-2565 |
Conference
Conference | 10th European Workshop on Structural Health Monitoring, EWSHM 2022 |
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Country/Territory | Italy |
City | Palermo |
Period | 4/07/22 → 7/07/22 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Acoustic emission technique
- Data fusion
- Machine learning
- Restricted-assess areas
- Thin PZT sensors