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

Conference paper (2023)

Authors

L. Cheng Steel & Composite Structures - CEG
A. Nokhbatolfoghahai Structural Integrity & Composites - Aerospace Engineering
R.M. Groves Structural Integrity & Composites - Aerospace Engineering
M. Veljkovic Steel & Composite Structures - CEG
Research Group
Steel & Composite Structures (CEG) (TU Delft)
Copyright: © 2023 L. Cheng, A. Nokhbatolfoghahai, R.M. Groves, M. Veljkovic
DOI: https://doi.org/10.1007/978-3-031-07254-3_62
Machine learning Acoustic emission technique Data fusion Restricted-assess areas Thin PZT sensors
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Published Date

2023

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Engineering Structures

Research Group

Steel & Composite Structures

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.