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Multisensor‐based hybrid empirical mode decomposition method towards system identification of structures
Author(s) -
Barbosh Mohamed,
Sadhu Ayan,
Vogrig Mike
Publication year - 2018
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2147
Subject(s) - hilbert–huang transform , modal , identification (biology) , mode (computer interface) , computer science , noise (video) , vibration , decomposition , algorithm , engineering , artificial intelligence , acoustics , computer vision , ecology , chemistry , botany , physics , filter (signal processing) , polymer chemistry , image (mathematics) , biology , operating system
Summary Multivariate empirical mode decomposition (MEMD) method is explored in this paper to perform modal identification of structures using the multisensor vibration data. Due to inherent sifting operation of empirical mode decomposition (EMD), the traditional MEMD results in mode‐mixing that causes significant inaccuracy in modal identification and condition assessment of structures. Independent component analysis, another powerful blind signal decomposition method, is integrated with the MEMD to alleviate mode‐mixing in the resulting modal responses. The proposed technique is verified using a suite of numerical, experimental, and full‐scale studies (a building tower in China and a long‐span bridge in Canada) considering several practical applications such as low‐energy frequencies, closely spaced modes, and measurement noise. The results confirm the improved performance of the proposed method and prove that it can be considered as a robust system identification tool for flexible structures.