Version 1
: Received: 9 September 2024 / Approved: 9 September 2024 / Online: 9 September 2024 (12:49:12 CEST)
How to cite:
Erlinger, T.; Kralovec, C.; Humer, C.; Schagerl, M. Impact of Model Knowledge on Acoustic Emission Source Localization Accuracy. Preprints2024, 2024090690. https://doi.org/10.20944/preprints202409.0690.v1
Erlinger, T.; Kralovec, C.; Humer, C.; Schagerl, M. Impact of Model Knowledge on Acoustic Emission Source Localization Accuracy. Preprints 2024, 2024090690. https://doi.org/10.20944/preprints202409.0690.v1
Erlinger, T.; Kralovec, C.; Humer, C.; Schagerl, M. Impact of Model Knowledge on Acoustic Emission Source Localization Accuracy. Preprints2024, 2024090690. https://doi.org/10.20944/preprints202409.0690.v1
APA Style
Erlinger, T., Kralovec, C., Humer, C., & Schagerl, M. (2024). Impact of Model Knowledge on Acoustic Emission Source Localization Accuracy. Preprints. https://doi.org/10.20944/preprints202409.0690.v1
Chicago/Turabian Style
Erlinger, T., Christoph Humer and Martin Schagerl. 2024 "Impact of Model Knowledge on Acoustic Emission Source Localization Accuracy" Preprints. https://doi.org/10.20944/preprints202409.0690.v1
Abstract
Reliable and precise damage localization in mechanical structures is of high importance in the context of structural health monitoring (SHM). The acoustic emission (AE) method has already shown its excellent suitability for damage localization. However, low signal-to-noise ratios (SNRs) are often prevalent in SHM, thus an increase in localization accuracy and robustness is still in demand. The present study faces this task through the integration of various model knowledge for AE source localization. The basis of the presented algorithm is the consideration of the dispersive behavior of elastic waves in thin-walled structures. The continuous wavelet transform (CWT) is used to obtain time-frequency representations of the signals, where frequency-dependent values for time-of-arrival (TOA) are extracted. Furthermore, the algorithm incorporates the knowledge that all sensors receive signals with the same time and location of origin, as well as the slightly different sensitivity of the theoretically equal sensors. The final localization results are achieved by a two-parameter grid search optimization. The algorithm is experimentally tested on a large aluminum plate with four piezoelectric wafer active sensors (PWASs) arranged in an array of 100 mm × 100 mm. The mean localization error of pencil lead breaks at ten different positions within the sensor array is used as an accuracy measure. It is shown that as more of the described model knowledge is incorporated into the localization, the accuracy increases. With the final algorithm, the mean localization error is more than halved compared to AE localization based on classical TOA estimation. Although the experiment described is conducted under laboratory conditions, the remarkable increase in accuracy suggests that AE source localization may be successful even at low SNR as typical for operational conditions.
Keywords
acoustic emission; structural health monitoring; localization; model knowledge; dispersion
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
