Impact identification using nonlinear dimensionality reduction and supervised learning. (1st October 2019)

• Record Type: Journal Article
• Authors: Meruane, V; Espinoza, C; López Droguett, E; Ortiz-Bernardin, A
• Is Part Of: Smart materials and structures. Volume 28:Number 11(2019:Nov.)
• Publication Date: 2019-10-01
• Subjects: impact identification -- impact identification, nonlinear dimensionality reduction techniques -- linear approximation with maximum entropy -- autoencoders; Smart materials -- Periodicals; Strucural design -- Periodicals; 620.11
• DOI: 10.1088/1361-665X/ab419e
• Languages: English
• ISSNs: 0964-1726

Abstract

Abstract: Real-time monitoring systems that can automatically locate and identify impacts as they occur have become increasingly attractive for ensuring safety and preventing catastrophic accidents in aerospace structures. In most cases, a set of piezoelectric transducers distributed over the structure captures strain–time data, which are preprocessed to extract relevant features that are fed to a supervised learning algorithm to detect, locate, and quantify impacts. The best results achieved to date in feature extraction for impact identification have been obtained with the use of principal component analysis (PCA). However, this technique cannot handle complex nonlinear data. The primary contribution of this study is the implementation of a novel impact identification algorithm that uses a supervised learning algorithm called linear approximation with maximum entropy (LME) in conjunction with different linear and nonlinear dimensionality reduction techniques, including PCA, kernel PCA, Isomap, local linear embedding (LLE), and multilayer autoencoders. The performance of LME with the different reduction techniques is tested with two experimental applications. The results show that the techniques that do not employ graphs, such as PCA, kernel PCA, and autoencoders, perform better, and the method that provides the best results is LME in conjunction with autoencoders. It is further demonstrated that LME with autoencoders works better than the algorithms available in the literature for similar problems.