A frequency domain blind identification method for operational modal analysis using a limited number of sensors

• Autor(a) principal: Li, Xinhui
• Data de Publicação: 2020
• Outros Autores: Antoni, Jerome, Brennan, Michael J. [UNESP], Yang, Tiejun, Liu, Zhigang
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1177/1077546319897218; http://hdl.handle.net/11449/195140
• topic: Operational modal analysis; second-order blind identification; blind source separation; independent component analysis; blind modal identification

Operational modal analysis is an experimental modal analysis approach, which uses vibration data collected when the structure is under operating conditions. Amongst the methods for operational modal analysis, blind source separation-based methods have been shown to be efficient and powerful. The existing blind source separation modal identification methods, however, require the number of sensors to be at least equal to the number of modes in the frequency range of interest to avoid spatial aliasing. In this article, a frequency domain algorithm that overcomes this problem is proposed, which is based on the joint diagonalization of a set of weighted covariance matrices. In the proposed approach, the frequency range of interest is partitioned into several frequency ranges in which the number of active modes in each band is less than the number of sensors. Numerical simulations and an experimental example demonstrate the efficacy of the method.