Issue |
MATEC Web Conf.
Volume 211, 2018
The 14th International Conference on Vibration Engineering and Technology of Machinery (VETOMAC XIV)
|
|
---|---|---|
Article Number | 21001 | |
Number of page(s) | 6 | |
Section | TP12: Structural health monitoring | |
DOI | https://doi.org/10.1051/matecconf/201821121001 | |
Published online | 10 October 2018 |
Non-destructive testing based on vibrations in the low to mid-frequency range
1
KU Leuven, Department of Mechanical Engineering,
Celestijnenlaan 300 - Box 2420,
3001
Leuven (Heverlee),
Belgium
2
DMMS lab, Flanders Make,
3001
Leuven (Heverlee),
Belgium
* e-mail: philip.becht@kuleuven.be
Recently, it has been shown that a Time Reversal MUltiple Signal Classification (TR-MUSIC) algorithm can be employed to detect defects in samples, which are both challenging in terms of material and in terms of geometrical complexity. This can be achieved by lowering the detection frequency as compared to most other TR and TR-MUSIC applications. In this case, the method operates in a low to mid-frequency range, where accurate models are still realizable. The method relies on the measurement of the frequency response function (FRF) between multiple excitation and sensor locations. These are gathered in a matrix, which then is decomposed in its singular vectors, serving as input for a MUSIC algorithm.
In order to improve the applicability of this method, it is shown how to adapt the algorithm, in order to reduce the number of excitation locations or the number of sensors significantly. This results in a considerable speed-up for the application of this non-destructive testing strategy. Furthermore, it is investigated numerically how the robustness of the detection result under the influence of random measurement errors behaves if the number of excitation/sensor locations is reduced. Additionally, the dependency between, the decrease of the number of excitation/sensor locations and the types of defects, which can be detected is studied.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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