Outlier Analysis of Nonlinear Solitary Waves for Health Monitoring Applications

Abstract

Structural health monitoring methods based on highly nonlinear solitary waves are emerging as a potential cost-effective technique to monitor or inspect a variety of structures. In the present study, the use of outlier analysis in the form of a discordancy test was investigated to enhance the damage detection capability of an HNSWs-based monitoring system. An experiment was conducted to detect simulated defects in a thick steel plate. HNSWs features were extracted and fed to a univariate analysis that compared the testing data to a set of baseline data. The results show that the outlier analysis improves the HNSWs ability to detect damage.