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A Local TR-MUSIC Algorithm for Damage Imaging of Aircraft Structures.
Fan, Shilei; Zhang, Aijia; Sun, Hu; Yun, Fenglin.
Afiliación
  • Fan S; College of Aerospace Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China.
  • Zhang A; COMAC Shanghai Aircraft Design & Research Institute, Shanghai 201210, China.
  • Sun H; School of Aerospace Engineering, Xiamen University, Xiamen 361005, China.
  • Yun F; School of Aerospace Engineering, Xiamen University, Xiamen 361005, China.
Sensors (Basel) ; 21(10)2021 May 11.
Article en En | MEDLINE | ID: mdl-34064934
ABSTRACT
Lamb wave-based damage imaging is a promising technique for aircraft structural health monitoring, as enhancing the resolution of damage detection is a persistent challenge. In this paper, a damage imaging technique based on the Time Reversal-MUltiple SIgnal Classification (TR-MUSIC) algorithm is developed to detect damage in plate-type structures. In the TR-MUSIC algorithm, a transfer matrix is first established by exciting and sensing signals. A TR operator is constructed for eigenvalue decomposition to divide the data space into signal and noise subspaces. The structural space spectrum of the algorithm is calculated based on the orthogonality of the two subspaces. A local TR-MUSIC algorithm is proposed to enhance the image quality of multiple damages by using a moving time window to establish the local space spectrum at different times or different distances. The multidamage detection capability of the proposed enhanced TR-MUSIC algorithm is verified by simulations and experiments. The results reveal that the local TR-MUSIC algorithm can not only effectively detect multiple damages in plate-type structures with good image quality but also has a superresolution ability for detecting damage with distances smaller than half the wavelength.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: China