A robust methodology for modal parameters estimation applied to SHM. (October 2017)

• Record Type: Journal Article
• Authors: Cardoso, Rharã; Cury, Alexandre; Barbosa, Flávio
• Is Part Of: Mechanical systems and signal processing. Volume 95(2017)
• Page Start: 24
• Page End: 41
• Publication Date: 2017-10
• Subjects: Structural health monitoring -- Automated modal identification -- Clustering -- Structural dynamics; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2017.03.021
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: An enhanced automated approach is developed for modal parameter estimation. New statistical techniques render the analysis more accurate and robust. Novel 2-step approach is validated through numerical and experimental applications. Abstract: The subject of structural health monitoring is drawing more and more attention over the last years. Many vibration-based techniques aiming at detecting small structural changes or even damage have been developed or enhanced through successive researches. Lately, several studies have focused on the use of raw dynamic data to assess information about structural condition. Despite this trend and much skepticism, many methods still rely on the use of modal parameters as fundamental data for damage detection. Therefore, it is of utmost importance that modal identification procedures are performed with a sufficient level of precision and automation. To fulfill these requirements, this paper presents a novel automated time-domain methodology to identify modal parameters based on a two-step clustering analysis. The first step consists in clustering modes estimates from parametric models of different orders, usually presented in stabilization diagrams. In an automated manner, the first clustering analysis indicates which estimates correspond to physical modes. To circumvent the detection of spurious modes or the loss of physical ones, a second clustering step is then performed. The second step consists in the data mining of information gathered from the first step. To attest the robustness and efficiency of the proposed methodology, numerically generated signals as well as experimental data obtained from a simply supported beam tested in laboratory and from a railway bridge are utilized. The results appeared to be more robust and accurate comparing to those obtained from methods based on one-step clustering analysis.