Operational Modal Analysis for Vibration Control Following Moving Window Locality Preserving Projections for Linear Slow-Time-Varying Structures

• Published in: Applied sciences Vol. 11; no. 2; p. 791
• Main Authors: Fu, Weihua, Wang, Cheng, Chen, Jianwei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2021
• Subjects: Aircraft; Algorithms; Cantilever beams; Degrees of freedom; Dynamic characteristics; Methods; Modal analysis; moving window; non-stationary vibration response signals; operational modal parameters; Parameter identification; preserving projections algorithm; Principal components analysis; Resonant frequencies; slow-time-varying structures; Vibration; Vibration analysis; Vibration control; Vibration response
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11020791

Modal parameters can reflect the dynamic characteristics of the structure and can be used to control vibration. To identify the operational modal parameters of linear slow-time-varying structures only from non-stationary vibration response signals, a method based on moving window locality preserving projections (MWLPP) algorithm is proposed. Based on the theory of “time freeze”, the method selects a fixed length window and takes the displacement response signal in each window as a stationary random sequence. The locality preserving projections algorithm is used to identify the transient modal frequency and modal shape of the structure at this window. The low-dimensional embedding of the displacement response data set calculated by locality preserving projections (LPP) corresponds to the modal coordinate response matrix, and the transformation matrix corresponds to the modal shape matrix. The simulation results of the mass slow-time-varying three degree of freedom (DOF) and the density slow-time-varying cantilever beam show that the new method can effectively identify the modal shape and modal natural frequency of the linear slow-time-varying only from the non-stationary vibration response signal, and the performance is better than the moving window principal component analysis (MWPCA).