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

• Published in: Applied sciences Vol. 11; no. 23; p. 11432
• Main Authors: Guo, Xiangying, Li, Changkun, Luo, Zhong, Cao, Dongxing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2021
• Subjects: Acceleration measurement; Accelerometers; Accuracy; Algorithms; Attenuation; Data smoothing; Displacement measurement; displacement reconstruction; Fourier transforms; frequency domain integration; Identification methods; Initial conditions; low-frequency attenuation; Methods; Modal analysis; modal parameter identification; Noise; Noise measurement; Parameter identification; Random errors; stochastic subspace identification; Stochasticity
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app112311432

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.