Affiliations: [a] College of Computer Science and Technology, Huaqiao University, Xiamen, China | [b] College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, China | [c] Department of Mathematics and Statistics, San Diego State University, San Diego, CA, USA
Correspondence:
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Corresponding author: Cheng Wang, College of Computer Science and Technology, Huaqiao University, Xiamen, China. E-mail: [email protected]
Abstract: In order to select the window function and window size adaptively before getting the results, we proposed adaptive moving window principle component analysis (AMWPCA) based OMA method to identify modal shapes and modal natural frequencies of slow LTV structures with weekly damped only from non-stationary vibration response signal online. The adaptive is achieved in two ways: change the window function or window size. We develop an adaptive indicator as the basis for window function and window size changes. Our adaptive approach is to make the difference between adjacent eigenvalues not too small. The operational modal parameter identification results in non-stationarity response signal dataset of a three-degree-of-freedom structure with slow time-varying mass show that comparing with fixed size moving window principle component analysis, our AMWPCA method can identify the modal shapes and modal frequencies better.
