A Time-Saving Stochastic Subspace Identification Method for Spatial Grid Structure.

In the identification process of SSI–DATA method, the stabilization diagram is generally used to determine the order of the system. However, in the modal parameter identification of spatial grid structure, there are few stable points that meet the requirements, and the stability axis is not obvious. So, the location of the modal stability point cannot be determined. Furthermore, the QR decomposition of the Hankel matrix is computationally inefficient and difficult to use for online identification when analyzing the spatial structure. In order to solve the above problems, this paper proposes a time-saving random subspace method of spatial grid structure based on an improved stabilization diagram. On the basis of the traditional random subspace method, the projection of the Hankel matrix is transformed into the projection transformation of its R matrix, and then the subsequent calculation is carried out. Then, the frequency search domain is set and the auxiliary stability axis is made. According to the spectrum peak, stabilization diagram and the auxiliary stability axis, the position of the stability axis is comprehensively judged. The modal parameters of the structure can be identified more accurately, which improves the calculation efficiency and avoids modal omissions or misjudgments to a certain extent. Using a spatial structure model (upper chord 18 × 12m, lower chord 16 × 10m, grid 2 × 1.5m), the advantages of this method were compared and verified. The results indicate that the calculation time of the traditional SSI–DATA method is 29.077s, while the improved calculation time is 3.752s, resulting in an 87.1% improvement in calculation efficiency, while also preventing mode omission and misjudgment. By further applying this method to vibration monitoring of spatial grid structure hangars, its applicability, higher computational efficiency, and precision have been proven. [ABSTRACT FROM AUTHOR]