Numerical enhancement of nonlinear model tracking for health monitoring

Abstract

Crack formation in a vibrating cantilever beam has been identified with the in situ nondestructive health monitoring Nonlinear Model Tracking (NMT) technique. The nonlinear cubic stiffness parameter has been chosen as the system’s dominant nonlinearity and has been tracked until catastrophic failure using a Continuous Time based system identification. The use of a nonlinear model allows for a range of healthy but complex system dynamics, such as changing natural frequency, which indicates a change in system health in traditional linear system health monitoring. Previous research has shown that significant change in the nonlinear parameter indicates a transition from a healthy to unhealthy system. The purpose of this study is to improve the robustness of the NMT method by investigating new data processing techniques. Numerical integration, regression fit with linear FRF, and strain gage sensors were introduced. The results of these new techniques were then compared with results from previous techniques to highlight effectiveness in determining change in a system’s health.