Modal Parameter Identification of Structures Using Reconstructed Displacements and Stochastic Subspace Identification

A method of modal parameter identification of structures using reconstructed displacements was proposed in the present research. The proposed method was developed based on the stochastic subspace identification (SSI) approach and used reconstructed displacements of measured accelerations as inputs. These reconstructed displacements suppressed the high-frequency component of measured acceleration data. Therefore, in comparison to the acceleration-based modal analysis, the operational modal analysis obtained more reliable and stable identification parameters from displacements regardless of the model order. However, due to the difficulty of displacement measurement, different types of noise interferences occurred when an acceleration sensor was used, causing a trend term drift error in the integral displacement. A moving average low-frequency attenuation frequency-domain integral was used to reconstruct displacements, and the moving time window was used in combination with the SSI method to identify the structural modal parameters. First, measured accelerations were used to estimate displacements. Due to the interference of noise and the influence of initial conditions, the integral displacement inevitably had a drift term. The moving average method was then used in combination with a filter to effectively eliminate the random fluctuation interference in measurement data and reduce the influence of random errors. Real displacement results of a structure were obtained through multiple smoothing, filtering, and integration. Finally, using reconstructed displacements as inputs, the improved SSI method was employed to identify the modal parameters of the structure.

Operational modal parameter identification with correlated colored noise excitation

Operational modal analysis refers to the modal analysis of a structure in its operating state. The advantage of operational modal analysis is that only the output vibration signal of a system is used. The classical operational modal analysis algorithm is based on the white noise excitation assumption, and it is considered that there is no correlation between the excitations; several identification methods have been developed in time and frequency domains. But excitations are not completely independent with each other and not pure white. In this article, the matrix theory is used to prove that the operational modal analysis algorithm can still be used to identify modal parameters when the excitation is correlated. In the simulation, five kinds of colored noise excitations are applied to the cantilever beam with correlated excitations, which shows that the idea proposed in this article is rational. In the experiment, the foundation excitation of colored noise is added to the cantilever beam, which can be regarded as applying several related excitations. It also shows the rationality of this idea.