A new approach for structural damage detection exploring the singular spectrum analysis. (May 2017)

• Record Type: Journal Article
• Authors: de Oliveira, Mario A; Vieira Filho, Jozue; Lopes, Vicente; Inman, Daniel J
• Is Part Of: Journal of intelligent material systems and structures. Volume 28:Number 9(2017)
• Page Start: 1160
• Page End: 1174
• Publication Date: 2017-05
• Subjects: Structural health monitoring -- time series analysis -- residues -- statistical signal processing -- principal component analysis -- lead zirconate titanate; Smart materials -- Periodicals; Intelligent control systems -- Periodicals; Artificial intelligence -- Periodicals; Matériaux intelligents -- Périodiques; Commande intelligente -- Périodiques; Intelligence artificielle -- Périodiques; 620.11
• DOI: 10.1177/1045389X16667549
• Languages: English
• ISSNs: 1045-389X

Abstract

This article presents a novel approach for damage detection applied to structural health monitoring systems exploring the residues obtained from singular spectrum analysis. In this technique, a lead zirconate titanate patch acting as actuator excites the structure, and three other patches are used as sensors to receive the structural responses. This method is based on a high-frequency excitation range in order to overcome the problem caused when the low-vibration modes are excited. In this method, a wideband chirp signal, with low amplitude and variable frequency, is used to excite the structure. The response signals are acquired in the time domain, and the singular spectrum analysis procedure is performed. The residues obtained between the reconstructed and original time series are used to compute statistical metrics. The residues calculated from singular spectrum analysis are used to compute the root mean square deviation and correlation coefficient deviation metric indices, rendering the damage detection approach more reliable. Tests were carried out on an aluminum plate, and the results have demonstrated the effectiveness of the proposed method making it an excellent approach for structural health monitoring applications. The results exploring different numbers of components used during the reconstruction process of time series are obtained, and the highlights are presented.