Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/37304
Title: Sparse Damage Detection with Complex Group Lasso and Adaptive Complex Group Lasso
Authors: Dimopoulos, Vasileios
Desmet, Wim
DECKERS, Elke 
Issue Date: 2022
Publisher: MDPI
Source: SENSORS, 22 (8) (Art N° 2978)
Abstract: Sparsity-based methods have recently come to the foreground of damage detection applications posing a robust and efficient alternative for traditional approaches. At the same time, low-frequency inspection is known to enable global monitoring with waves propagating over large distances. In this paper, a single sensor complex Group Lasso methodology for the problem of structural defect localization by means of compressive sensing and complex low-frequency response functions is presented. The complex Group Lasso methodology is evaluated on composite plates with induced scatterers. An adaptive setting of the methodology is also proposed to further enhance resolution. Results from both approaches are compared with a full-array, super-resolution MUSIC technique of the same signal model. Both algorithms are shown to demonstrate high and competitive performance.
Notes: Dimopoulos, V (corresponding author), Katholieke Univ Leuven, Dept Mech Engn, Celestijnenlaan 300, B-3001 Leuven, Belgium.; Dimopoulos, V (corresponding author), Flanders Make, DMMS Lab, B-3001 Leuven, Belgium.
vasileios.dimopoulos@kuleuven.be; wim.desmet@kuleuven.be;
elke.deckers@kuleuven.be
Keywords: sparse damage detection; complex Group Lasso; adaptive complex Group;Lasso; low-frequency inspection
Document URI: http://hdl.handle.net/1942/37304
e-ISSN: 1424-8220
DOI: 10.3390/s22082978
ISI #: WOS:000785119100001
Rights: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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