Dalian University of Technology

Xiaojun Yao

Hongnan Li

Tinghua Yi

Chunxu Qu

Structural health monitoring providing sparse component analysis method for structural modal identification with incomplete number of sensors. Transforming structural acceleration response data into time frequency domain by short time Fourier transform, detecting time frequency points contributed by only one-order mode where real and imaginary parts have the same direction, and taking the detection result as the initial result of single-source-points; refining the initial result of detection of single-source-point located near the peak of power spectral density, and clustering single-source-points to obtain a mode shape matrix; constructing generalized spectral matrixes using short time Fourier transform coefficients, conducting singular value decomposition on generalized spectral matrix at a single-source-point, taking the first singular value as an auto-spectrum of single-order mode, obtaining the frequency of each order by picking the peak of auto-spectrum, and extracting damping ratio of each order by transforming the auto-spectrum into a time domain through inverse Fourier transform. By means of this method, structural modal parameters are obtained when the number of sensors is incomplete, thereby increasing the identification accuracy of the sparse component analysis method.

Sparse component analysis method for structural modal identification when the number of sensors is incomplete

Data analysis for structural health monitoring relating to a method of modal identification for structures with non-proportional damping based on extended sparse component analysis. Hilbert transform constructs analytical signal of acceleration response. Analytical signal is transformed into time-frequency domain using short-time-Fourier transform. The criterion is taken as the correlation coefficient of adjacent frequency points is close to 1. Points contributed by only one mode are detected from the time-frequency plane. Phases calculated at single-source-points are used to remove local outliers through local outlier factor method. Amplitudes of complex-valued mode shapes are estimated by Hierarchical clustering of amplitudes for time-frequency coefficients at single-source-points. Averaged phases of grouped single-source-points are estimated phases of complex-valued mode shapes. Finally, complex-valued mode shapes are acquired. Modal responses are estimated by sparse reconstruction method. This method extends application range of sparse component analysis method, and can identify complex modes of non-proportionally damped structures.

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Liaoning, China

Xiaojun Yao