Application of recursive SSA as data pre-processing filter for stochastic subspace identification

Abstract

The objective of this paper is to develop on-line system parameter estimation and damage detection technique from the response measurements through using the Recursive Covariance-Driven Stochastic Subspace identification (RSSI-COV) approach. To reduce the effect of noise on the results of identification, discussion on the pre-processing of data using recursive singular spectrum analysis (rSSA) is presented to remove the noise contaminant measurements so as to enhance the stability of data analysis. Through the application of rSSA-SSI-COV to the vibration measurement of bridge during scouring experiment, the ability of the proposed algorithm was proved to be robust to the noise perturbations and offers a very good online tracking capability. The accuracy and robustness offered by rSSA-SSI-COV provides a key to obtain the evidence of imminent bridge settlement and a very stable modal frequency tracking which makes it possible for early warning. The peak values of the identified <TEX>$1^{st}$</TEX> mode shape slope ratio has shown to be a good indicator for damage location, meanwhile, the drastic movements of the peak of <TEX>$2^{nd}$</TEX> mode slope ratio could be used as another feature to indicate imminent pier settlement.