An improved stochastic subspace identification for operational modal analysis

An improved stochastic subspace identification algorithm is introduced to solve the low computational efficiency problem of the Data-driven stochastic subspace identification. Compared with the conventional algorithm, it needs much less cost of memory and computing time because it does not have a process of the QR decomposition of Hankel matrix. Model similarity index is proposed to measure the reliability of the modes obtained by the improved stochastic subspace identification. Furthermore, the stabilization diagram in combination with the modal similarity index is adopted to effectively indicate spurious modes resulting from noise and model redundancy. A criterion named the modal norm is introduced to indicate the dominating mode. A numerical example on the parameter estimation of a linear time-invariant system of 7 degrees of freedom and one experimental example on the parameter estimation of Chaotianmen bridge model in Chongqing are presented to demonstrate the efficacy of the method.

► Modify SSI algorithm to improve the computational efficiency.
► Propose a criterion to indicate the spurious modes obtained by SSI.
► Introduce the modal norm to indicate the dominating mode.
► The improved algorithm was used to identify the modal parameters of a bridge model.