Sequential clustering of synchrosqueezed wavelet transform coefficients for efficient modal identification
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Time–frequency-based signal processing for efficient modal parameter identification is the theme of this paper. For this purpose, recently developed synchrosqueezed transformation is adopted along with sequential clustering to bypass heuristic intermittency required in traditional wavelet transform-based modal identification. Here, k-means algorithm is used to locate the energy content of the recorded response in different frequency scales, where synchrosqueezing offers better resolution. The rationale behind the use of the unsupervised learning lies in its ability to segregate the energy content of the signals without any requirement of prior training. Two validation exercises are presented to establish the performance of the proposed identification strategy. Finally, the response of a thin beam tested in the laboratory is used that has large number of active modes with many of them are closely spaced. Results presented in this paper clearly proves the efficiency of the proposed algorithm that can be adopted for modal identification for structural health monitoring and control.
KeywordsWavelet transformation Synchrosqueezed transformation k-means cluster Modal identification Generalized morse wavelet
Authors hereby acknowledge Prof. A. Dutta and Prof. S. Deb for sharing the data and other details of BRNS building. Authors also acknowledge Prof. S. Adhikari for sharing the data and images of experimental setup of thin beam.
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