Acoustic Emission-Based Detection in Restricted-Access Areas Using Multiple PZT Disc Sensors

Conference Paper (2023)
Author(s)

Lu Cheng (TU Delft - Civil Engineering & Geosciences)

Ali Nokhbatolfoghahai (TU Delft - Aerospace Engineering)

Roger M. Groves (TU Delft - Aerospace Engineering)

Milan Veljkovic (TU Delft - Civil Engineering & Geosciences)

Research Group
Steel & Composite Structures
DOI related publication
https://doi.org/10.1007/978-3-031-07254-3_62 Final published version
More Info
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Publication Year
2023
Language
English
Research Group
Steel & Composite Structures
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-care Otherwise 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.
Pages (from-to)
619-629
Publisher
Springer
ISBN (print)
978-3-031-07253-6
ISBN (electronic)
978-3-031-07254-3
Event
10th European Workshop on Structural Health Monitoring, EWSHM 2022 (2022-07-04 - 2022-07-07), Palermo, Italy
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333
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Institutional Repository
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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.

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