Synchrosqueezing transform-based identification of time-varying structural systems using multi-sensor data. (10th November 2020)

• Record Type: Journal Article
• Authors: Sony, Sandeep; Sadhu, Ayan
• Is Part Of: Journal of sound and vibration. Volume 486(2020)
• Publication Date: 2020-11-10
• Subjects: Structural health monitoring -- Modal identification -- Synchrosqueezing transform -- Progressive damage -- MEMD -- Time-varying systems; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2020.115576
• Languages: English
• ISSNs: 0022-460X

Abstract

Abstract: In this paper, a hybrid time-frequency method combining Synchrosqueezing Transform (SST) and Multivariate Empirical Mode Decomposition (MEMD) is proposed for the identification of time-varying structural systems. MEMD is first used to decompose the closely spaced modal responses of structures into mono components, and SST is then used to track time-varying modal frequencies with a better time-frequency resolution, thereby improving the capability of both methods. A single degree-of-freedom model is first used to study the sensitivity for damage duration and frequency resolution of the SST under measurement noise. The proposed method is then validated using a multi-degree-of-freedom simulation model, an experimental model, and a full-scale study using MIT Green Building data. The results show that the proposed method has successfully identified time-varying frequencies of various types of structures under different extent of measurement noise. Highlights: This paper explores SST-assisted MEMD for the identification of time-varying systems. The proposed method is capable of identifying both discrete and progressive damage. The method is insensitive to measurement noise and the duration of damages. A suite of experimental and full-scale studies is used to illustrate the proposed method.