Spatiotemporal denoising for structural dynamic response monitoring data

Engineering typically deploys multi-points to capture structural dynamic response signals which are vulnerable to noise interference. Non-stationary random vibration makes most signal feature-based denoising algorithms unsuitable for dynamic response signals ' denoising. Truncated singular value decomposition is an extensively applied method in this field for fewer requested parameters and less dependence on signals, but originally designed for single-point and fails to take the spatial correlation of multi-points into account. This paper extended it and proposed a multivariate denoising algorithm suitable for structural dynamic response multipoints monitoring data. Firstly, singular value decomposition was processed on each signal element in multipoints monitoring data produced the decomposed signals. Then, singular value decomposition was performed again on the obtained decomposed signals in the time and spatial domains. Finally, it were determined that truncation points for multivariate signals denoising by hypothesis test on their singular value spectrums. The proposed algorithm ' s denoising performance was compared with four other denoising algorithms through several instance calculations. Instance calculations involved two different data sets of structural acceleration response monitoring data under seismic excitation: the simulated data of a frame structure and the measured data from a certain gravity dam. The proposed algorithm exhibited almost optimal denoising performance, particularly in processing colored noise. Compared with four other algorithms involved, the proposed algorithm averagely improved denoising assessment indicators by- 3.166%, 83.984%, 78.844% and 24.888% for white noise, as well as 30.562%, 103.311%, 103.695% and 35.124% for colored noise respectively. The proposed algorithm is effective and feasible, with significant advantages in the structural dynamic response multi-points monitoring data denoising. It extends the truncated singular value decomposition algorithm suitable for univariate denoising in structural dynamic response to multivariate denoising, expands its application scope, improves the algorithm ' s performance, and makes the selection of the parameter in the algorithm: the truncation points more reasonable. It is of great significance for improving the accuracy and reliability of structural health monitoring.