Machine learning algorithms for damage detection: Kernel-based approaches. (17th February 2016)

• Record Type: Journal Article
• Authors: Santos, Adam; Figueiredo, Eloi; Silva, M.F.M.; Sales, C.S.; Costa, J.C.W.A.
• Is Part Of: Journal of sound and vibration. Volume 363(2016)
• Page Start: 584
• Page End: 599
• Publication Date: 2016-02-17
• Subjects: Structural health monitoring -- Damage detection -- Kernel -- Operational conditions -- Environmental conditions; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2015.11.008
• Languages: English
• ISSNs: 0022-460X

Abstract

Abstract: This paper presents four kernel-based algorithms for damage detection under varying operational and environmental conditions, namely based on one-class support vector machine, support vector data description, kernel principal component analysis and greedy kernel principal component analysis. Acceleration time-series from an array of accelerometers were obtained from a laboratory structure and used for performance comparison. The main contribution of this study is the applicability of the proposed algorithms for damage detection as well as the comparison of the classification performance between these algorithms and other four ones already considered as reliable approaches in the literature. All proposed algorithms revealed to have better classification performance than the previous ones.